A Discrete-Event Network Simulator
Models

ns-3 Model Library

This is the ns-3 Model Library documentation. Primary documentation for the ns-3 project is available in five forms:

This document is written in reStructuredText for Sphinx and is maintained in the doc/models directory of ns-3’s source code.

Organization

This manual compiles documentation for ns-3 models and supporting software that enable users to construct network simulations. It is important to distinguish between modules and models:

  • ns-3 software is organized into separate modules that are each built as a separate software library. Individual ns-3 programs can link the modules (libraries) they need to conduct their simulation.
  • ns-3 models are abstract representations of real-world objects, protocols, devices, etc.

An ns-3 module may consist of more than one model (for instance, the internet module contains models for both TCP and UDP). In general, ns-3 models do not span multiple software modules, however.

This manual provides documentation about the models of ns-3. It complements two other sources of documentation concerning models:

  • the model APIs are documented, from a programming perspective, using Doxygen. Doxygen for ns-3 models is available on the project web server.
  • the ns-3 core is documented in the developer’s manual. ns-3 models make use of the facilities of the core, such as attributes, default values, random numbers, test frameworks, etc. Consult the main web site to find copies of the manual.

Finally, additional documentation about various aspects of ns-3 may exist on the project wiki.

A sample outline of how to write model library documentation can be found by executing the create-module.py program and looking at the template created in the file new-module/doc/new-module.rst.

$ cd src
$ ./create-module.py new-module

The remainder of this document is organized alphabetically by module name.

If you are new to ns-3, you might first want to read below about the network module, which contains some fundamental models for the simulator. The packet model, models for different address formats, and abstract base classes for objects such as nodes, net devices, channels, sockets, and applications are discussed there.

Animation

Animation is an important tool for network simulation. While ns-3 does not contain a default graphical animation tool, we currently have two ways to provide animation, namely using the PyViz method or the NetAnim method. The PyViz method is described in http://www.nsnam.org/wiki/PyViz.

We will describe the NetAnim method briefly here.

NetAnim

NetAnim is a standalone, Qt4-based software executable that uses a trace file generated during an ns-3 simulation to display the topology and animate the packet flow between nodes.

_images/NetAnim_3_105.png

An example of packet animation on wired-links

In addition, NetAnim also provides useful features such as tables to display meta-data of packets like the image below

_images/PacketStatistics.png

An example of tables for packet meta-data with protocol filters

A way to visualize the trajectory of a mobile node

_images/Trajectory.png

An example of the trajectory of a mobile node

A way to display the routing-tables of multiple nodes at various points in time

_images/RoutingTables.png

A way to display counters associated with multiple nodes as a chart or a table

_images/NodeCountersChart.png
_images/NodeCountersTable.png

A way to view the timeline of packet transmit and receive events

_images/PacketTimeline.png

Methodology

The class ns3::AnimationInterface is responsible for the creation the trace XML file. AnimationInterface uses the tracing infrastructure to track packet flows between nodes. AnimationInterface registers itself as a trace hook for tx and rx events before the simulation begins. When a packet is scheduled for transmission or reception, the corresponding tx and rx trace hooks in AnimationInterface are called. When the rx hooks are called, AnimationInterface will be aware of the two endpoints between which a packet has flowed, and adds this information to the trace file, in XML format along with the corresponding tx and rx timestamps. The XML format will be discussed in a later section. It is important to note that AnimationInterface records a packet only if the rx trace hooks are called. Every tx event must be matched by an rx event.

Downloading NetAnim

If NetAnim is not already available in the ns-3 package you downloaded, you can do the following:

Please ensure that you have installed mercurial. The latest version of NetAnim can be downloaded using mercurial with the following command:

$ hg clone http://code.nsnam.org/netanim

Building NetAnim

Prerequisites

Qt4 (4.7 and over) is required to build NetAnim. This can be obtained using the following ways:

For Debian/Ubuntu Linux distributions:

$ apt-get install qt4-dev-tools

For Red Hat/Fedora based distribution:

$ yum install qt4
$ yum install qt4-devel

For Mac/OSX, see http://qt.nokia.com/downloads/

Build steps

To build NetAnim use the following commands:

$ cd netanim
$ make clean
$ qmake NetAnim.pro  (For MAC Users: qmake -spec macx-g++ NetAnim.pro)
$ make

Note: qmake could be “qmake-qt4” in some systems

This should create an executable named “NetAnim” in the same directory:

 $ ls -l NetAnim
-rwxr-xr-x 1 john john 390395 2012-05-22 08:32 NetAnim

Usage

Using NetAnim is a two-step process

Step 1:Generate the animation XML trace file during simulation using “ns3::AnimationInterface” in the ns-3 code base.

Step 2:Load the XML trace file generated in Step 1 with the offline Qt4-based animator named NetAnim.

Step 1: Generate XML animation trace file

The class “AnimationInterface” under “src/netanim” uses underlying ns-3 trace sources to construct a timestamped ASCII file in XML format.

Examples are found under src/netanim/examples Example:

$ ./waf -d debug configure --enable-examples
$ ./waf --run "dumbbell-animation"

The above will create an XML file dumbbell-animation.xml

Mandatory
  1. Ensure that your program’s wscript includes the “netanim” module. An example of such a wscript is at src/netanim/examples/wscript.
  2. Include the header [#include “ns3/netanim-module.h”] in your test program
  3. Add the statement
AnimationInterface anim ("animation.xml");  // where "animation.xml" is any arbitrary filename

[for versions before ns-3.13 you also have to use the line “anim.SetXMLOutput() to set the XML mode and also use anim.StartAnimation();]

Optional

The following are optional but useful steps:

// Step 1
anim.SetMobilityPollInterval (Seconds (1));

AnimationInterface records the position of all nodes every 250 ms by default. The statement above sets the periodic interval at which AnimationInterface records the position of all nodes. If the nodes are expected to move very little, it is useful to set a high mobility poll interval to avoid large XML files.

// Step 2
anim.SetConstantPosition (Ptr< Node > n, double x, double y);

AnimationInterface requires that the position of all nodes be set. In ns-3 this is done by setting an associated MobilityModel. “SetConstantPosition” is a quick way to set the x-y coordinates of a node which is stationary.

// Step 3
anim.SetStartTime (Seconds(150)); and anim.SetStopTime (Seconds(150));

AnimationInterface can generate large XML files. The above statements restricts the window between which AnimationInterface does tracing. Restricting the window serves to focus only on relevant portions of the simulation and creating manageably small XML files

// Step 4
AnimationInterface anim ("animation.xml", 50000);

Using the above constructor ensures that each animation XML trace file has only 50000 packets. For example, if AnimationInterface captures 150000 packets, using the above constructor splits the capture into 3 files

  • animation.xml - containing the packet range 1-50000
  • animation.xml-1 - containing the packet range 50001-100000
  • animation.xml-2 - containing the packet range 100001-150000
// Step 5
anim.EnablePacketMetadata (true);

With the above statement, AnimationInterface records the meta-data of each packet in the xml trace file. Metadata can be used by NetAnim to provide better statistics and filter, along with providing some brief information about the packet such as TCP sequence number or source & destination IP address during packet animation.

CAUTION: Enabling this feature will result in larger XML trace files. Please do NOT enable this feature when using Wimax links.

// Step 6
anim.UpdateNodeDescription (5, "Access-point");

With the above statement, AnimationInterface assigns the text “Access-point” to node 5.

// Step 7
anim.UpdateNodeSize (6, 1.5, 1.5);

With the above statement, AnimationInterface sets the node size to scale by 1.5. NetAnim automatically scales the graphics view to fit the oboundaries of the topology. This means that NetAnim, can abnormally scale a node’s size too high or too low. Using AnimationInterface::UpdateNodeSize allows you to overwrite the default scaling in NetAnim and use your own custom scale.

// Step 8
anim.UpdateNodeCounter (89, 7, 3.4);

With the above statement, AnimationInterface sets the counter with Id == 89, associated with Node 7 with the value 3.4. The counter with Id 89 is obtained using AnimationInterface::AddNodeCounter. An example usage for this is in src/netanim/examples/resources_demo.cc.

Step 2: Loading the XML in NetAnim
  1. Assuming NetAnim was built, use the command ”./NetAnim” to launch NetAnim. Please review the section “Building NetAnim” if NetAnim is not available.
  2. When NetAnim is opened, click on the File open button at the top-left corner, select the XML file generated during Step 1.
  3. Hit the green play button to begin animation.

Here is a video illustrating this http://www.youtube.com/watch?v=tz_hUuNwFDs

Wiki

For detailed instructions on installing “NetAnim”, F.A.Qs and loading the XML trace file (mentioned earlier) using NetAnim please refer: http://www.nsnam.org/wiki/NetAnim

Antenna Module

Design documentation

Overview

The Antenna module provides:

  1. a new base class (AntennaModel) that provides an interface for the modeling of the radiation pattern of an antenna;
  2. a set of classes derived from this base class that each models the radiation pattern of different types of antennas.

AntennaModel

The AntennaModel uses the coordinate system adopted in [Balanis] and depicted in Figure Coordinate system of the AntennaModel. This system is obtained by traslating the cartesian coordinate system used by the ns-3 MobilityModel into the new origin o which is the location of the antenna, and then transforming the coordinates of every generic point p of the space from cartesian coordinates (x,y,z) into spherical coordinates (r, \theta,\phi). The antenna model neglects the radial component r, and only considers the angle components (\theta, \phi). An antenna radiation pattern is then expressed as a mathematical function g(\theta, \phi) \longrightarrow \mathcal{R} that returns the gain (in dB) for each possible direction of transmission/reception. All angles are expressed in radians.

_images/antenna-coordinate-system.png

Coordinate system of the AntennaModel

Provided models

In this section we describe the antenna radiation pattern models that are included within the antenna module.

IsotropicAntennaModel

This antenna radiation pattern model provides a unitary gain (0 dB) for all direction.

CosineAntennaModel

This is the cosine model described in [Chunjian]: the antenna gain is determined as:

g(\phi, \theta) = \cos^{n} \left(\frac{\phi - \phi_{0}}{2}  \right)

where \phi_{0} is the azimuthal orientation of the antenna (i.e., its direction of maximum gain) and the exponential

n = -\frac{3}{20 \log_{10} \left( \cos \frac{\phi_{3dB}}{4} \right)}

determines the desired 3dB beamwidth \phi_{3dB}. Note that this radiation pattern is independent of the inclination angle \theta.

A major difference between the model of [Chunjian] and the one implemented in the class CosineAntennaModel is that only the element factor (i.e., what described by the above formulas) is considered. In fact, [Chunjian] also considered an additional antenna array factor. The reason why the latter is excluded is that we expect that the average user would desire to specify a given beamwidth exactly, without adding an array factor at a latter stage which would in practice alter the effective beamwidth of the resulting radiation pattern.

ParabolicAntennaModel

This model is based on the parabolic approximation of the main lobe radiation pattern. It is often used in the context of cellular system to model the radiation pattern of a cell sector, see for instance [R4-092042a] and [Calcev]. The antenna gain in dB is determined as:

g_{dB}(\phi, \theta) = -\min \left( 12 \left(\frac{\phi  - \phi_{0}}{\phi_{3dB}} \right)^2, A_{max} \right)

where \phi_{0} is the azimuthal orientation of the antenna (i.e., its direction of maximum gain), \phi_{3dB} is its 3 dB beamwidth, and A_{max} is the maximum attenuation in dB of the antenna. Note that this radiation pattern is independent of the inclination angle \theta.

[Balanis]C.A. Balanis, “Antenna Theory - Analysis and Design”, Wiley, 2nd Ed.
[Chunjian](1, 2, 3) Li Chunjian, “Efficient Antenna Patterns for Three-Sector WCDMA Systems”, Master of Science Thesis, Chalmers University of Technology, Göteborg, Sweden, 2003
[Calcev]George Calcev and Matt Dillon, “Antenna Tilt Control in CDMA Networks”, in Proc. of the 2nd Annual International Wireless Internet Conference (WICON), 2006
[R4-092042a]3GPP TSG RAN WG4 (Radio) Meeting #51, R4-092042, Simulation assumptions and parameters for FDD HeNB RF requirements.

User Documentation

The antenna moduled can be used with all the wireless technologies and physical layer models that support it. Currently, this includes the physical layer models based on the SpectrumPhy. Please refer to the documentation of each of these models for details.

Testing Documentation

In this section we describe the test suites included with the antenna module that verify its correct functionality.

Angles

The unit test suite angles verifies that the Angles class is constructed properly by correct conversion from 3D cartesian coordinates according to the available methods (construction from a single vector and from a pair of vectors). For each method, several test cases are provided that compare the values (\phi, \theta) determied by the constructor to known reference values. The test passes if for each case the values are equal to the reference up to a tolerance of 10^{-10} which accounts for numerical errors.

DegreesToRadians

The unit test suite degrees-radians verifies that the methods DegreesToRadians and RadiansToDegrees work properly by comparing with known reference values in a number of test cases. Each test case passes if the comparison is equal up to a tolerance of 10^{-10} which accounts for numerical errors.

IsotropicAntennaModel

The unit test suite isotropic-antenna-model checks that the IsotropicAntennaModel class works properly, i.e., returns always a 0dB gain regardless of the direction.

CosineAntennaModel

The unit test suite cosine-antenna-model checks that the CosineAntennaModel class works properly. Several test cases are provided that check for the antenna gain value calculated at different directions and for different values of the orientation, the reference gain and the beamwidth. The reference gain is calculated by hand. Each test case passes if the reference gain in dB is equal to the value returned by CosineAntennaModel within a tolerance of 0.001, which accounts for the approximation done for the calculation of the reference values.

ParabolicAntennaModel

The unit test suite parabolic-antenna-model checks that the ParabolicAntennaModel class works properly. Several test cases are provided that check for the antenna gain value calculated at different directions and for different values of the orientation, the maximum attenuation and the beamwidth. The reference gain is calculated by hand. Each test case passes if the reference gain in dB is equal to the value returned by ParabolicAntennaModel within a tolerance of 0.001, which accounts for the approximation done for the calculation of the reference values.

Ad Hoc On-Demand Distance Vector (AODV)

This model implements the base specification of the Ad Hoc On-Demand Distance Vector (AODV) protocol. The implementation is based on RFC 3561.

The model was written by Elena Buchatskaia and Pavel Boyko of ITTP RAS, and is based on the ns-2 AODV model developed by the CMU/MONARCH group and optimized and tuned by Samir Das and Mahesh Marina, University of Cincinnati, and also on the AODV-UU implementation by Erik Nordström of Uppsala University.

Model Description

The source code for the AODV model lives in the directory src/aodv.

Design

Class ns3::aodv::RoutingProtocol implements all functionality of service packet exchange and inherits from ns3::Ipv4RoutingProtocol. The base class defines two virtual functions for packet routing and forwarding. The first one, ns3::aodv::RouteOutput, is used for locally originated packets, and the second one, ns3::aodv::RouteInput, is used for forwarding and/or delivering received packets.

Protocol operation depends on many adjustable parameters. Parameters for this functionality are attributes of ns3::aodv::RoutingProtocol. Parameter default values are drawn from the RFC and allow the enabling/disabling protocol features, such as broadcasting HELLO messages, broadcasting data packets and so on.

AODV discovers routes on demand. Therefore, the AODV model buffers all packets while a route request packet (RREQ) is disseminated. A packet queue is implemented in aodv-rqueue.cc. A smart pointer to the packet, ns3::Ipv4RoutingProtocol::ErrorCallback, ns3::Ipv4RoutingProtocol::UnicastForwardCallback, and the IP header are stored in this queue. The packet queue implements garbage collection of old packets and a queue size limit.

The routing table implementation supports garbage collection of old entries and state machine, defined in the standard. It is implemented as a STL map container. The key is a destination IP address.

Some elements of protocol operation aren’t described in the RFC. These elements generally concern cooperation of different OSI model layers. The model uses the following heuristics:

  • This AODV implementation can detect the presence of unidirectional links and avoid them if necessary. If the node the model receives an RREQ for is a neighbor, the cause may be a unidirectional link. This heuristic is taken from AODV-UU implementation and can be disabled.
  • Protocol operation strongly depends on broken link detection mechanism. The model implements two such heuristics. First, this implementation support HELLO messages. However HELLO messages are not a good way to perform neighbor sensing in a wireless environment (at least not over 802.11). Therefore, one may experience bad performance when running over wireless. There are several reasons for this: 1) HELLO messages are broadcasted. In 802.11, broadcasting is often done at a lower bit rate than unicasting, thus HELLO messages can travel further than unicast data. 2) HELLO messages are small, thus less prone to bit errors than data transmissions, and 3) Broadcast transmissions are not guaranteed to be bidirectional, unlike unicast transmissions. Second, we use layer 2 feedback when possible. Link are considered to be broken if frame transmission results in a transmission failure for all retries. This mechanism is meant for active links and works faster than the first method.

The layer 2 feedback implementation relies on the TxErrHeader trace source, currently supported in AdhocWifiMac only.

Scope and Limitations

The model is for IPv4 only. The following optional protocol optimizations are not implemented:

  1. Expanding ring search.
  2. Local link repair.
  3. RREP, RREQ and HELLO message extensions.

These techniques require direct access to IP header, which contradicts the assertion from the AODV RFC that AODV works over UDP. This model uses UDP for simplicity, hindering the ability to implement certain protocol optimizations. The model doesn’t use low layer raw sockets because they are not portable.

Future Work

No announced plans.

References

Usage

Examples

Helpers

Attributes

Tracing

Logging

Caveats

Validation

Unit tests

Larger-scale performance tests

Applications

Placeholder chapter

Bridge NetDevice

Placeholder chapter

Some examples of the use of Bridge NetDevice can be found in examples/csma/ directory.

BRITE Integration

This model implements an interface to BRITE, the Boston university Representative Internet Topology gEnerator [1]. BRITE is a standard tool for generating realistic internet topologies. The ns-3 model, described herein, provides a helper class to facilitate generating ns-3 specific topologies using BRITE configuration files. BRITE builds the original graph which is stored as nodes and edges in the ns-3 BriteTopolgyHelper class. In the ns-3 integration of BRITE, the generator generates a topology and then provides access to leaf nodes for each AS generated. ns-3 users can than attach custom topologies to these leaf nodes either by creating them manually or using topology generators provided in ns-3.

There are three major types of topologies available in BRITE: Router, AS, and Hierarchical which is a combination of AS and Router. For the purposes of ns-3 simulation, the most useful are likely to be Router and Hierarchical. Router level topologies be generated using either the Waxman model or the Barabasi-Albert model. Each model has different parameters that effect topology creation. For flat router topologies, all nodes are considered to be in the same AS.

BRITE Hierarchical topologies contain two levels. The first is the AS level. This level can be also be created by using either the Waxman model or the Barabasi-Albert model. Then for each node in the AS topology, a router level topology is constructed. These router level topologies can again either use the Waxman model or the Barbasi-Albert model. BRITE interconnects these separate router topologies as specified by the AS level topology. Once the hierarchical topology is constructed, it is flattened into a large router level topology.

Further information can be found in the BRITE user manual: http://www.cs.bu.edu/brite/publications/usermanual.pdf

Model Description

The model relies on building an external BRITE library, and then building some ns-3 helpers that call out to the library. The source code for the ns-3 helpers lives in the directory src/brite/helper.

Design

To generate the BRITE topology, ns-3 helpers call out to the external BRITE library, and using a standard BRITE configuration file, the BRITE code builds a graph with nodes and edges according to this configuration file. Please see the BRITE documenation or the example configuration files in src/brite/examples/conf_files to get a better grasp of BRITE configuration options. The graph built by BRITE is returned to ns-3, and a ns-3 implementation of the graph is built. Leaf nodes for each AS are available for the user to either attach custom topologies or install ns-3 applications directly.

References

[1]Alberto Medina, Anukool Lakhina, Ibrahim Matta, and John Byers. BRITE: An Approach to Universal Topology Generation. In Proceedings of the International Workshop on Modeling, Analysis and Simulation of Computer and Telecommunications Systems- MASCOTS ‘01, Cincinnati, Ohio, August 2001.

Usage

The brite-generic-example can be referenced to see basic usage of the BRITE interface. In summary, the BriteTopologyHelper is used as the interface point by passing in a BRITE configuration file. Along with the configuration file a BRITE formatted random seed file can also be passed in. If a seed file is not passed in, the helper will create a seed file using ns-3’s UniformRandomVariable. Once the topology has been generated by BRITE, BuildBriteTopology() is called to create the ns-3 representation. Next IP Address can be assigned to the topology using either AssignIpv4Addresses() or AssignIpv6Addresses(). It should be noted that each point-to-point link in the topology will be treated as a new network therefore for IPV4 a /30 subnet should be used to avoid wasting a large amount of the available address space.

Example BRITE configuration files can be found in /src/brite/examples/conf_files/. ASBarbasi and ASWaxman are examples of AS only topologies. The RTBarabasi and RTWaxman files are examples of router only topologies. Finally the TD_ASBarabasi_RTWaxman configuration file is an example of a Hierarchical topology that uses the Barabasi-Albert model for the AS level and the Waxman model for each of the router level topologies. Information on the BRITE parameters used in these files can be found in the BRITE user manual.

Building BRITE Integration

The first step is to download and build the ns-3 specific BRITE repository:

$ hg clone http://code.nsnam.org/BRITE
$ cd BRITE
$ make

This will build BRITE and create a library, libbrite.so, within the BRITE directory.

Once BRITE has been built successfully, we proceed to configure ns-3 with BRITE support. Change to your ns-3 directory:

$ ./waf configure --with-brite=/your/path/to/brite/source --enable-examples

Make sure it says ‘enabled’ beside ‘BRITE Integration’. If it does not, then something has gone wrong. Either you have forgotten to build BRITE first following the steps above, or ns-3 could not find your BRITE directory.

Next, build ns-3:

$ ./waf

Examples

For an example demonstrating BRITE integration run:

$ ./waf --run 'brite-generic-example'

By enabling the verbose parameter, the example will print out the node and edge information in a similar format to standard BRITE output. There are many other command-line parameters including confFile, tracing, and nix, described below:

confFile
A BRITE configuration file. Many different BRITE configuration file examples exist in the src/brite/examples/conf_files directory, for example, RTBarabasi20.conf and RTWaxman.conf. Please refer to the conf_files directory for more examples.
tracing
Enables ascii tracing.
nix
Enables nix-vector routing. Global routing is used by default.

The generic BRITE example also support visualization using pyviz, assuming python bindings in ns-3 are enabled:

$ ./waf --run brite-generic-example --vis

Simulations involving BRITE can also be used with MPI. The total number of MPI instances is passed to the BRITE topology helper where a modulo divide is used to assign the nodes for each AS to a MPI instance. An example can be found in src/brite/examples:

$ mpirun -np 2 ./waf --run brite-MPI-example

Please see the ns-3 MPI documentation for information on setting up MPI with ns-3.

Buildings Module

cd .. include:: replace.txt

Design documentation

Overview

The Buildings module provides:

  1. a new class (Building) that models the presence of a building in a simulation scenario;
  2. a new class (MobilityBuildingInfo) that allows to specify the location, size and characteristics of buildings present in the simulated area, and allows the placement of nodes inside those buildings;
  3. a container class with the definition of the most useful pathloss models and the correspondent variables called BuildingsPropagationLossModel.
  4. a new propagation model (HybridBuildingsPropagationLossModel) working with the mobility model just introduced, that allows to model the phenomenon of indoor/outdoor propagation in the presence of buildings.
  5. a simplified model working only with Okumura Hata (OhBuildingsPropagationLossModel) considering the phenomenon of indoor/outdoor propagation in the presence of buildings.

The models have been designed with LTE in mind, though their implementation is in fact independent from any LTE-specific code, and can be used with other ns-3 wireless technologies as well (e.g., wifi, wimax).

The HybridBuildingsPropagationLossModel pathloss model included is obtained through a combination of several well known pathloss models in order to mimic different environmental scenarios such as urban, suburban and open areas. Moreover, the model considers both outdoor and indoor indoor and outdoor communication has to be included since HeNB might be installed either within building and either outside. In case of indoor communication, the model has to consider also the type of building in outdoor <-> indoor communication according to some general criteria such as the wall penetration losses of the common materials; moreover it includes some general configuration for the internal walls in indoor communications.

The OhBuildingsPropagationLossModel pathloss model has been created for simplifying the previous one removing the thresholds for switching from one model to other. For doing this it has been used only one propagation model from the one available (i.e., the Okumura Hata). The presence of building is still considered in the model; therefore all the considerations of above regarding the building type are still valid. The same consideration can be done for what concern the environmental scenario and frequency since both of them are parameters of the model considered.

The Building class

The model includes a specific class called Building which contains a ns3 Box class for defining the dimension of the building. In order to implements the characteristics of the pathloss models included, the Building class supports the following attributes:

  • building type:
    • Residential (default value)
    • Office
    • Commercial
  • external walls type
    • Wood
    • ConcreteWithWindows (default value)
    • ConcreteWithoutWindows
    • StoneBlocks
  • number of floors (default value 1, which means only ground-floor)
  • number of rooms in x-axis (default value 1)
  • number of rooms in y-axis (default value 1)

The Building class is based on the following assumptions:

  • a buildings is represented as a rectangular parallelepiped (i.e., a box)
  • the walls are parallel to the x, y, and z axis
  • a building is divided into a grid of rooms, identified by the following parameters:
    • number of floors
    • number of rooms along the x-axis
    • number of rooms along the y-axis
  • the z axis is the vertical axis, i.e., floor numbers increase for increasing z axis values
  • the x and y room indices start from 1 and increase along the x and y axis respectively
  • all rooms in a building have equal size

The MobilityBuildingInfo class

The MobilityBuildingInfo class, which inherits from the ns3 class Object, is in charge of maintaining information about the position of a node with respect to building. The information managed by MobilityBuildingInfo is:

  • whether the node is indoor or outdoor
  • if indoor:
    • in which building the node is
    • in which room the node is positioned (x, y and floor room indices)

The class MobilityBuildingInfo is used by BuildingsPropagationLossModel class, which inherits from the ns3 class PropagationLossModel and manages the pathloss computation of the single components and their composition according to the nodes’ positions. Moreover, it implements also the shadowing, that is the loss due to obstacles in the main path (i.e., vegetation, buildings, etc.).

It is to be noted that, MobilityBuildingInfo can be used by any other propagation model. However, based on the information at the time of this writing, only the ones defined in the building module are designed for considering the constraints introduced by the buildings.

ItuR1238PropagationLossModel

This class implements a building-dependent indoor propagation loss model based on the ITU P.1238 model, which includes losses due to type of building (i.e., residential, office and commercial). The analytical expression is given in the following.

L_\mathrm{total} = 20\log f + N\log d + L_f(n)- 28 [dB]

where:

N = \left\{ \begin{array}{lll} 28 & residential \\ 30 & office \\ 22 & commercial\end{array} \right. : power loss coefficient [dB]

L_f = \left\{ \begin{array}{lll} 4n & residential \\ 15+4(n-1) & office \\ 6+3(n-1) & commercial\end{array} \right.

n : number of floors between base station and mobile (n\ge 1)

f : frequency [MHz]

d : distance (where d > 1) [m]

BuildingsPropagationLossModel

The BuildingsPropagationLossModel provides an additional set of building-dependent pathloss model elements that are used to implement different pathloss logics. These pathloss model elements are described in the following subsections.

External Wall Loss (EWL)

This component models the penetration loss through walls for indoor to outdoor communications and vice-versa. The values are taken from the [cost231] model.

  • Wood ~ 4 dB
  • Concrete with windows (not metallized) ~ 7 dB
  • Concrete without windows ~ 15 dB (spans between 10 and 20 in COST231)
  • Stone blocks ~ 12 dB
Internal Walls Loss (IWL)

This component models the penetration loss occurring in indoor-to-indoor communications within the same building. The total loss is calculated assuming that each single internal wall has a constant penetration loss L_{siw}, and approximating the number of walls that are penetrated with the manhattan distance (in number of rooms) between the transmitter and the receiver. In detail, let x_1, y_1, x_2, y_2 denote the room number along the x and y axis respectively for user 1 and 2; the total loss L_{IWL} is calculated as

L_{IWL} = L_{siw} (|x_1 -x_2| + |y_1 - y_2|)

Height Gain Model (HG)

This component model the gain due to the fact that the transmitting device is on a floor above the ground. In the literature [turkmani] this gain has been evaluated as about 2 dB per floor. This gain can be applied to all the indoor to outdoor communications and vice-versa.

Shadowing Model

The shadowing is modeled according to a log-normal distribution with variable standard deviation as function of the relative position (indoor or outdoor) of the MobilityModel instances involved. One random value is drawn for each pair of MobilityModels, and stays constant for that pair during the whole simulation. Thus, the model is appropriate for static nodes only.

The model considers that the mean of the shadowing loss in dB is always 0. For the variance, the model considers three possible values of standard deviation, in detail:

  • outdoor (m_shadowingSigmaOutdoor, defaul value of 7 dB) \rightarrow X_\mathrm{O} \sim N(\mu_\mathrm{O}, \sigma_\mathrm{O}^2).
  • indoor (m_shadowingSigmaIndoor, defaul value of 10 dB) \rightarrow X_\mathrm{I} \sim N(\mu_\mathrm{I}, \sigma_\mathrm{I}^2).
  • external walls penetration (m_shadowingSigmaExtWalls, default value 5 dB) \rightarrow X_\mathrm{W} \sim N(\mu_\mathrm{W}, \sigma_\mathrm{W}^2)

The simulator generates a shadowing value per each active link according to nodes’ position the first time the link is used for transmitting. In case of transmissions from outdoor nodes to indoor ones, and vice-versa, the standard deviation (\sigma_\mathrm{IO}) has to be calculated as the square root of the sum of the quadratic values of the standard deviatio in case of outdoor nodes and the one for the external walls penetration. This is due to the fact that that the components producing the shadowing are independent of each other; therefore, the variance of a distribution resulting from the sum of two independent normal ones is the sum of the variances.

X \sim N(\mu,\sigma^2) \mbox{ and } Y \sim N(\nu,\tau^2)

Z = X + Y \sim Z (\mu + \nu, \sigma^2 + \tau^2)

\Rightarrow \sigma_\mathrm{IO} = \sqrt{\sigma_\mathrm{O}^2 + \sigma_\mathrm{W}^2}

Pathloss logics

In the following we describe the different pathloss logic that are implemented by inheriting from BuildingsPropagationLossModel.

HybridBuildingsPropagationLossModel

The HybridBuildingsPropagationLossModel pathloss model included is obtained through a combination of several well known pathloss models in order to mimic different outdoor and indoor scenarios, as well as indoor-to-outdoor and outdoor-to-indoor scenarios. In detail, the class HybridBuildingsPropagationLossModel integrates the following pathloss models:

  • OkumuraHataPropagationLossModel (OH) (at frequencies > 2.3 GHz substituted by Kun2600MhzPropagationLossModel)
  • ItuR1411LosPropagationLossModel and ItuR1411NlosOverRooftopPropagationLossModel (I1411)
  • ItuR1238PropagationLossModel (I1238)
  • the pathloss elements of the BuildingsPropagationLossModel (EWL, HG, IWL)

The following pseudo-code illustrates how the different pathloss model elements described above are integrated in HybridBuildingsPropagationLossModel:

if (txNode is outdoor)
  then
    if (rxNode is outdoor)
      then
        if (distance > 1 km)
          then
            if (rxNode or txNode is below the rooftop)
              then
                L = I1411
              else
                L = OH
          else
            L = I1411
      else (rxNode is indoor)
        if (distance > 1 km)
          then
            if (rxNode or txNode is below the rooftop)
              L = I1411 + EWL + HG
            else
              L = OH + EWL + HG
          else
            L = I1411 + EWL + HG
else (txNode is indoor)
  if (rxNode is indoor)
    then
     if (same building)
        then
          L = I1238 + IWL
        else
          L = I1411 + 2*EWL
   else (rxNode is outdoor)
    if (distance > 1 km)
      then
        if (rxNode or txNode is below the rooftop)
              then
                L = I1411 + EWL + HG
              else
                L = OH + EWL + HG
      else
        L = I1411 + EWL

We note that, for the case of communication between two nodes below rooftop level with distance is greater then 1 km, we still consider the I1411 model, since OH is specifically designed for macro cells and therefore for antennas above the roof-top level.

For the ITU-R P.1411 model we consider both the LOS and NLoS versions. In particular, we considers the LoS propagation for distances that are shorted than a tunable threshold (m_itu1411NlosThreshold). In case on NLoS propagation, the over the roof-top model is taken in consideration for modeling both macro BS and SC. In case on NLoS several parameters scenario dependent have been included, such as average street width, orientation, etc. The values of such parameters have to be properly set according to the scenario implemented, the model does not calculate natively their values. In case any values is provided, the standard ones are used, apart for the height of the mobile and BS, which instead their integrity is tested directly in the code (i.e., they have to be greater then zero). In the following we give the expressions of the components of the model.

We also note that the use of different propagation models (OH, I1411, I1238 with their variants) in HybridBuildingsPropagationLossModel can result in discontinuities of the pathloss with respect to distance. A proper tuning of the attributes (especially the distance threshold attributes) can avoid these discontinuities. However, since the behavior of each model depends on several other parameters (frequency, node heigth, etc), there is no default value of these thresholds that can avoid the discontinuities in all possible configurations. Hence, an appropriate tuning of these parameters is left to the user.

OhBuildingsPropagationLossModel

The OhBuildingsPropagationLossModel class has been created as a simple means to solve the discontinuity problems of HybridBuildingsPropagationLossModel without doing scenario-specific parameter tuning. The solution is to use only one propagation loss model (i.e., Okumura Hata), while retaining the structure of the pathloss logic for the calculation of other path loss components (such as wall penetration losses). The result is a model that is free of discontinuities (except those due to walls), but that is less realistic overall for a generic scenario with buildings and outdoor/indoor users, e.g., because Okumura Hata is not suitable neither for indoor communications nor for outdoor communications below rooftop level.

In detail, the class OhBuildingsPropagationLossModel integrates the following pathloss models:

  • OkumuraHataPropagationLossModel (OH)
  • the pathloss elements of the BuildingsPropagationLossModel (EWL, HG, IWL)

The following pseudo-code illustrates how the different pathloss model elements described above are integrated in OhBuildingsPropagationLossModel:

if (txNode is outdoor)
  then
    if (rxNode is outdoor)
      then
        L = OH
      else (rxNode is indoor)
        L = OH + EWL
else (txNode is indoor)
  if (rxNode is indoor)
    then
     if (same building)
        then
          L = OH + IWL
        else
          L = OH + 2*EWL
   else (rxNode is outdoor)
      L = OH + EWL

We note that OhBuildingsPropagationLossModel is a significant simplification with respect to HybridBuildingsPropagationLossModel, due to the fact that OH is used always. While this gives a less accurate model in some scenarios (especially below rooftop and indoor), it effectively avoids the issue of pathloss discontinuities that affects HybridBuildingsPropagationLossModel.

User Documentation

How to use buildings in a simulation

In this section we explain the basic usage of the buildings model within a simulation program.

Include the headers

Add this at the beginning of your simulation program:

#include <ns3/buildings-module.h>
Create a building

As an example, let’s create a residential 10 x 20 x 10 building:

double x_min = 0.0;
double x_max = 10.0;
double y_min = 0.0;
double y_max = 20.0;
double z_min = 0.0;
double z_max = 10.0;
Ptr<Building> b = CreateObject <Building> ();
b->SetBoundaries (Box (x_min, x_max, y_min, y_max, z_min, z_max));
b->SetBuildingType (Building::Residential);
b->SetExtWallsType (Building::ConcreteWithWindows);
b->SetNFloors (3);
b->SetNRoomsX (3);
b->SetNRoomsY (2);

This building has three floors and an internal 3 x 2 grid of rooms of equal size.

The helper class GridBuildingAllocator is also available to easily create a set of buildings with identical characteristics placed on a rectangular grid. Here’s an example of how to use it:

Ptr<GridBuildingAllocator>  gridBuildingAllocator;
gridBuildingAllocator = CreateObject<GridBuildingAllocator> ();
gridBuildingAllocator->SetAttribute ("GridWidth", UintegerValue (3));
gridBuildingAllocator->SetAttribute ("LengthX", DoubleValue (7));
gridBuildingAllocator->SetAttribute ("LengthY", DoubleValue (13));
gridBuildingAllocator->SetAttribute ("DeltaX", DoubleValue (3));
gridBuildingAllocator->SetAttribute ("DeltaY", DoubleValue (3));
gridBuildingAllocator->SetAttribute ("Height", DoubleValue (6));
gridBuildingAllocator->SetBuildingAttribute ("NRoomsX", UintegerValue (2));
gridBuildingAllocator->SetBuildingAttribute ("NRoomsY", UintegerValue (4));
gridBuildingAllocator->SetBuildingAttribute ("NFloors", UintegerValue (2));
gridBuildingAllocator->SetAttribute ("MinX", DoubleValue (0));
gridBuildingAllocator->SetAttribute ("MinY", DoubleValue (0));
gridBuildingAllocator->Create (6);

This will create a 3x2 grid of 6 buildings, each 7 x 13 x 6 m with 2 x 4 rooms inside and 2 foors; the buildings are spaced by 3 m on both the x and the y axis.

Setup nodes and mobility models

Nodes and mobility models are configured as usual, however in order to use them with the buildings model you need an additional call to BuildingsHelper::Install(), so as to let the mobility model include the informtion on their position w.r.t. the buildings. Here is an example:

MobilityHelper mobility;
mobility.SetMobilityModel ("ns3::ConstantPositionMobilityModel");
ueNodes.Create (2);
mobility.Install (ueNodes);
BuildingsHelper::Install (ueNodes);

It is to be noted that any mobility model can be used. However, the user is advised to make sure that the behavior of the mobility model being used is consistent with the presence of Buildings. For example, using a simple random mobility over the whole simulation area in presence of buildings might easily results in node moving in and out of buildings, regardless of the presence of walls.

Place some nodes

You can place nodes in your simulation using several methods, which are described in the following.

Legacy positioning methods

Any legacy ns-3 positioning method can be used to place node in the simulation. The important additional step is to For example, you can place nodes manually like this:

Ptr<ConstantPositionMobilityModel> mm0 = enbNodes.Get (0)->GetObject<ConstantPositionMobilityModel> ();
Ptr<ConstantPositionMobilityModel> mm1 = enbNodes.Get (1)->GetObject<ConstantPositionMobilityModel> ();
mm0->SetPosition (Vector (5.0, 5.0, 1.5));
mm1->SetPosition (Vector (30.0, 40.0, 1.5));

MobilityHelper mobility;
mobility.SetMobilityModel ("ns3::ConstantPositionMobilityModel");
ueNodes.Create (2);
mobility.Install (ueNodes);
BuildingsHelper::Install (ueNodes);
mm0->SetPosition (Vector (5.0, 5.0, 1.5));
mm1->SetPosition (Vector (30.0, 40.0, 1.5));

Alternatively, you could use any existing PositionAllocator class. The coordinates of the node will determine whether it is placed outdoor or indoor and, if indoor, in which building and room it is placed.

Building-specific positioning methods

The following position allocator classes are available to place node in special positions with respect to buildings:

  • RandomBuildingPositionAllocator: Allocate each position by randomly chosing a building from the list of all buildings, and then randomly chosing a position inside the building.
  • RandomRoomPositionAllocator: Allocate each position by randomly chosing a room from the list of rooms in all buildings, and then randomly chosing a position inside the room.
  • SameRoomPositionAllocator: Walks a given NodeContainer sequentially, and for each node allocate a new position randomly in the same room of that node.
  • FixedRoomPositionAllocator: Generate a random position uniformly distributed in the volume of a chosen room inside a chosen building.
Make the Mobility Model Consistent

Important: whenever you use buildings, you have to issue the following command after we have placed all nodes and buildings in the simulation:

BuildingsHelper::MakeMobilityModelConsistent ();

This command will go through the lists of all nodes and of all buildings, determine for each user if it is indoor or outdoor, and if indoor it will also determine the building in which the user is located and the corresponding floor and number inside the building.

Building-aware pathloss model

After you placed buildings and nodes in a simulation, you can use a building-aware pathloss model in a simulation exactly in the same way you would use any regular path loss model. How to do this is specific for the wireless module that you are considering (lte, wifi, wimax, etc.), so please refer to the documentation of that model for specific instructions.

Main configurable attributes

The Building class has the following configurable parameters:

  • building type: Residential, Office and Commercial.
  • external walls type: Wood, ConcreteWithWindows, ConcreteWithoutWindows and StoneBlocks.
  • building bounds: a Box class with the building bounds.
  • number of floors.
  • number of rooms in x-axis and y-axis (rooms can be placed only in a grid way).

The BuildingMobilityLossModel parameter configurable with the ns3 attribute system is represented by the bound (string Bounds) of the simulation area by providing a Box class with the area bounds. Moreover, by means of its methos the following parameters can be configured:

  • the number of floor the node is placed (default 0).
  • the position in the rooms grid.

The BuildingPropagationLossModel class has the following configurable parameters configurable with the attribute system:

  • Frequency: reference frequency (default 2160 MHz), note that by setting the frequency the wavelength is set accordingly automatically and viceversa).
  • Lambda: the wavelength (0.139 meters, considering the above frequency).
  • ShadowSigmaOutdoor: the standard deviation of the shadowing for outdoor nodes (defaul 7.0).
  • ShadowSigmaIndoor: the standard deviation of the shadowing for indoor nodes (default 8.0).
  • ShadowSigmaExtWalls: the standard deviation of the shadowing due to external walls penetration for outdoor to indoor communications (default 5.0).
  • RooftopLevel: the level of the rooftop of the building in meters (default 20 meters).
  • Los2NlosThr: the value of distance of the switching point between line-of-sigth and non-line-of-sight propagation model in meters (default 200 meters).
  • ITU1411DistanceThr: the value of distance of the switching point between short range (ITU 1211) communications and long range (Okumura Hata) in meters (default 200 meters).
  • MinDistance: the minimum distance in meters between two nodes for evaluating the pathloss (considered neglictible before this threshold) (default 0.5 meters).
  • Environment: the environment scenario among Urban, SubUrban and OpenAreas (default Urban).
  • CitySize: the dimension of the city among Small, Medium, Large (default Large).

In order to use the hybrid mode, the class to be used is the HybridBuildingMobilityLossModel, which allows the selection of the proper pathloss model according to the pathloss logic presented in the design chapter. However, this solution has the problem that the pathloss model switching points might present discontinuities due to the different characteristics of the model. This implies that according to the specific scenario, the threshold used for switching have to be properly tuned. The simple OhBuildingMobilityLossModel overcome this problem by using only the Okumura Hata model and the wall penetration losses.

Testing Documentation

Overview

To test and validate the ns-3 Building Pathloss module, some test suites is provided which are integrated with the ns-3 test framework. To run them, you need to have configured the build of the simulator in this way:

$ ./waf configure --enable-tests --enable-modules=buildings
$ ./test.py

The above will run not only the test suites belonging to the buildings module, but also those belonging to all the other ns-3 modules on which the buildings module depends. See the ns-3 manual for generic information on the testing framework.

You can get a more detailed report in HTML format in this way:

$ ./test.py -w results.html

After the above command has run, you can view the detailed result for each test by opening the file results.html with a web browser.

You can run each test suite separately using this command:

$ ./test.py -s test-suite-name

For more details about test.py and the ns-3 testing framework, please refer to the ns-3 manual.

Description of the test suites

BuildingsHelper test

The test suite buildings-helper checks that the method BuildingsHelper::MakeAllInstancesConsistent () works properly, i.e., that the BuildingsHelper is successful in locating if nodes are outdoor or indoor, and if indoor that they are located in the correct building, room and floor. Several test cases are provided with different buildings (having different size, position, rooms and floors) and different node positions. The test passes if each every node is located correctly.

BuildingPositionAllocator test

The test suite building-position-allocator feature two test cases that check that respectively RandomRoomPositionAllocator and SameRoomPositionAllocator work properly. Each test cases involves a single 2x3x2 room building (total 12 rooms) at known coordinates and respectively 24 and 48 nodes. Both tests check that the number of nodes allocated in each room is the expected one and that the position of the nodes is also correct.

Buildings Pathloss tests

The test suite buildings-pathloss-model provides different unit tests that compare the expected results of the buildings pathloss module in specific scenarios with pre calculated values obtained offline with an Octave script (test/reference/buildings-pathloss.m). The tests are considered passed if the two values are equal up to a tolerance of 0.1, which is deemed appropriate for the typical usage of pathloss values (which are in dB).

In the following we detailed the scenarios considered, their selection has been done for covering the wide set of possible pathloss logic combinations. The pathloss logic results therefore implicitly tested.

Test #1 Okumura Hata

In this test we test the standard Okumura Hata model; therefore both eNB and UE are placed outside at a distance of 2000 m. The frequency used is the E-UTRA band #5, which correspond to 869 MHz (see table 5.5-1 of 36.101). The test includes also the validation of the areas extensions (i.e., urban, suburban and open-areas) and of the city size (small, medium and large).

Test #2 COST231 Model

This test is aimed at validating the COST231 model. The test is similar to the Okumura Hata one, except that the frequency used is the EUTRA band #1 (2140 MHz) and that the test can be performed only for large and small cities in urban scenarios due to model limitations.

Test #3 2.6 GHz model

This test validates the 2.6 GHz Kun model. The test is similar to Okumura Hata one except that the frequency is the EUTRA band #7 (2620 MHz) and the test can be performed only in urban scenario.

Test #4 ITU1411 LoS model

This test is aimed at validating the ITU1411 model in case of line of sight within street canyons transmissions. In this case the UE is placed at 100 meters far from the eNB, since the threshold for switching between LoS and NLoS is left to default one (i.e., 200 m.).

Test #5 ITU1411 NLoS model

This test is aimed at validating the ITU1411 model in case of non line of sight over the rooftop transmissions. In this case the UE is placed at 900 meters far from the eNB, in order to be above the threshold for switching between LoS and NLoS is left to default one (i.e., 200 m.).

Test #6 ITUP1238 model

This test is aimed at validating the ITUP1238 model in case of indoor transmissions. In this case both the UE and the eNB are placed in a residential building with walls made of concrete with windows. Ue is placed at the second floor and distances 30 meters far from the eNB, which is placed at the first floor.

Test #7 Outdoor -> Indoor with Okumura Hata model

This test validates the outdoor to indoor transmissions for large distances. In this case the UE is placed in a residential building with wall made of concrete with windows and distances 2000 meters from the outdoor eNB.

Test #8 Outdoor -> Indoor with ITU1411 model

This test validates the outdoor to indoor transmissions for short distances. In this case the UE is placed in a residential building with walls made of concrete with windows and distances 100 meters from the outdoor eNB.

Test #9 Indoor -> Outdoor with ITU1411 model

This test validates the outdoor to indoor transmissions for very short distances. In this case the eNB is placed in the second floor of a residential building with walls made of concrete with windows and distances 100 meters from the outdoor UE (i.e., LoS communication). Therefore the height gain has to be included in the pathloss evaluation.

Test #10 Indoor -> Outdoor with ITU1411 model

This test validates the outdoor to indoor transmissions for short distances. In this case the eNB is placed in the second floor of a residential building with walls made of concrete with windows and distances 500 meters from the outdoor UE (i.e., NLoS communication). Therefore the height gain has to be included in the pathloss evaluation.

Buildings Shadowing Test

The test suite buildings-shadowing-test is a unit test intended to verify the statistical distribution of the shadowing model implemented by BuildingsPathlossModel. The shadowing is modeled according to a normal distribution with mean \mu = 0 and variable standard deviation \sigma, according to models commonly used in literature. Three test cases are provided, which cover the cases of indoor, outdoor and indoor-to-outdoor communications. Each test case generates 1000 different samples of shadowing for different pairs of MobilityModel instances in a given scenario. Shadowing values are obtained by subtracting from the total loss value returned by HybridBuildingsPathlossModel the path loss component which is constant and pre-determined for each test case. The test verifies that the sample mean and sample variance of the shadowing values fall within the 99% confidence interval of the sample mean and sample variance. The test also verifies that the shadowing values returned at successive times for the same pair of MobilityModel instances is constant.

References

[turkmani]Turkmani A.M.D., J.D. Parson and D.G. Lewis, “Radio propagation into buildings at 441, 900 and 1400 MHz”, in Proc. of 4th Int. Conference on Land Mobile Radio, 1987.

Click Modular Router Integration

Click is a software architecture for building configurable routers. By using different combinations of packet processing units called elements, a Click router can be made to perform a specific kind of functionality. This flexibility provides a good platform for testing and experimenting with different protocols.

Model Description

The source code for the Click model lives in the directory src/click.

Design

ns-3’s design is well suited for an integration with Click due to the following reasons:

  • Packets in ns-3 are serialised/deserialised as they move up/down the stack. This allows ns-3 packets to be passed to and from Click as they are.
  • This also means that any kind of ns-3 traffic generator and transport should work easily on top of Click.
  • By striving to implement click as an Ipv4RoutingProtocol instance, we can avoid significant changes to the LL and MAC layer of the ns-3 code.

The design goal was to make the ns-3-click public API simple enough such that the user needs to merely add an Ipv4ClickRouting instance to the node, and inform each Click node of the Click configuration file (.click file) that it is to use.

This model implements the interface to the Click Modular Router and provides the Ipv4ClickRouting class to allow a node to use Click for external routing. Unlike normal Ipv4RoutingProtocol sub types, Ipv4ClickRouting doesn’t use a RouteInput() method, but instead, receives a packet on the appropriate interface and processes it accordingly. Note that you need to have a routing table type element in your Click graph to use Click for external routing. This is needed by the RouteOutput() function inherited from Ipv4RoutingProtocol. Furthermore, a Click based node uses a different kind of L3 in the form of Ipv4L3ClickProtocol, which is a trimmed down version of Ipv4L3Protocol. Ipv4L3ClickProtocol passes on packets passing through the stack to Ipv4ClickRouting for processing.

Developing a Simulator API to allow ns-3 to interact with Click

Much of the API is already well defined, which allows Click to probe for information from the simulator (like a Node’s ID, an Interface ID and so forth). By retaining most of the methods, it should be possible to write new implementations specific to ns-3 for the same functionality.

Hence, for the Click integration with ns-3, a class named Ipv4ClickRouting will handle the interaction with Click. The code for the same can be found in src/click/model/ipv4-click-routing.{cc,h}.

Packet hand off between ns-3 and Click

There are four kinds of packet hand-offs that can occur between ns-3 and Click.

  • L4 to L3
  • L3 to L4
  • L3 to L2
  • L2 to L3

To overcome this, we implement Ipv4L3ClickProtocol, a stripped down version of Ipv4L3Protocol. Ipv4L3ClickProtocol passes packets to and from Ipv4ClickRouting appropriately to perform routing.

Scope and Limitations

  • In its current state, the NS-3 Click Integration is limited to use only with L3, leaving NS-3 to handle L2. We are currently working on adding Click MAC support as well. See the usage section to make sure that you design your Click graphs accordingly.
  • Furthermore, ns-3-click will work only with userlevel elements. The complete list of elements are available at http://read.cs.ucla.edu/click/elements. Elements that have ‘all’, ‘userlevel’ or ‘ns’ mentioned beside them may be used.
  • As of now, the ns-3 interface to Click is Ipv4 only. We will be adding Ipv6 support in the future.

References

  • Eddie Kohler, Robert Morris, Benjie Chen, John Jannotti, and M. Frans Kaashoek. The click modular router. ACM Transactions on Computer Systems 18(3), August 2000, pages 263-297.
  • Lalith Suresh P., and Ruben Merz. Ns-3-click: click modular router integration for ns-3. In Proc. of 3rd International ICST Workshop on NS-3 (WNS3), Barcelona, Spain. March, 2011.
  • Michael Neufeld, Ashish Jain, and Dirk Grunwald. Nsclick: bridging network simulation and deployment. MSWiM ‘02: Proceedings of the 5th ACM international workshop on Modeling analysis and simulation of wireless and mobile systems, 2002, Atlanta, Georgia, USA. http://doi.acm.org/10.1145/570758.570772

Usage

Building Click

The first step is to clone Click from the github repository and build it:

$ git clone https://github.com/kohler/click
$ cd click/
$ ./configure --disable-linuxmodule --enable-nsclick --enable-wifi
$ make

The –enable-wifi flag may be skipped if you don’t intend on using Click with Wifi. * Note: You don’t need to do a ‘make install’.

Once Click has been built successfully, change into the ns-3 directory and configure ns-3 with Click Integration support:

$ ./waf configure --enable-examples --enable-tests --with-nsclick=/path/to/click/source

Hint: If you have click installed one directory above ns-3 (such as in the ns-3-allinone directory), and the name of the directory is ‘click’ (or a symbolic link to the directory is named ‘click’), then the –with-nsclick specifier is not necessary; the ns-3 build system will successfully find the directory.

If it says ‘enabled’ beside ‘NS-3 Click Integration Support’, then you’re good to go. Note: If running modular ns-3, the minimum set of modules required to run all ns-3-click examples is wifi, csma and config-store.

Next, try running one of the examples:

$ ./waf --run nsclick-simple-lan

You may then view the resulting .pcap traces, which are named nsclick-simple-lan-0-0.pcap and nsclick-simple-lan-0-1.pcap.

Click Graph Instructions

The following should be kept in mind when making your Click graph:

  • Only userlevel elements can be used.
  • You will need to replace FromDevice and ToDevice elements with FromSimDevice and ToSimDevice elements.
  • Packets to the kernel are sent up using ToSimDevice(tap0,IP).
  • For any node, the device which sends/receives packets to/from the kernel, is named ‘tap0’. The remaining interfaces should be named eth0, eth1 and so forth (even if you’re using wifi). Please note that the device numbering should begin from 0. In future, this will be made flexible so that users can name devices in their Click file as they wish.
  • A routing table element is a mandatory. The OUTports of the routing table element should correspond to the interface number of the device through which the packet will ultimately be sent out. Violating this rule will lead to really weird packet traces. This routing table element’s name should then be passed to the Ipv4ClickRouting protocol object as a simulation parameter. See the Click examples for details.
  • The current implementation leaves Click with mainly L3 functionality, with ns-3 handling L2. We will soon begin working to support the use of MAC protocols on Click as well. This means that as of now, Click’s Wifi specific elements cannot be used with ns-3.

Debugging Packet Flows from Click

From any point within a Click graph, you may use the Print (http://read.cs.ucla.edu/click/elements/print) element and its variants for pretty printing of packet contents. Furthermore, you may generate pcap traces of packets flowing through a Click graph by using the ToDump (http://read.cs.ucla.edu/click/elements/todump) element as well. For instance:

myarpquerier
 -> Print(fromarpquery,64)
 -> ToDump(out_arpquery,PER_NODE 1)
 -> ethout;

and ...will print the contents of packets that flow out of the ArpQuerier, then generate a pcap trace file which will have a suffix ‘out_arpquery’, for each node using the Click file, before pushing packets onto ‘ethout’.

Helper

To have a node run Click, the easiest way would be to use the ClickInternetStackHelper class in your simulation script. For instance:

ClickInternetStackHelper click;
click.SetClickFile (myNodeContainer, "nsclick-simple-lan.click");
click.SetRoutingTableElement (myNodeContainer, "u/rt");
click.Install (myNodeContainer);

The example scripts inside src/click/examples/ demonstrate the use of Click based nodes in different scenarios. The helper source can be found inside src/click/helper/click-internet-stack-helper.{h,cc}

Examples

The following examples have been written, which can be found in src/click/examples/:

  • nsclick-simple-lan.cc and nsclick-raw-wlan.cc: A Click based node communicating with a normal ns-3 node without Click, using Csma and Wifi respectively. It also demonstrates the use of TCP on top of Click, something which the original nsclick implementation for NS-2 couldn’t achieve.
  • nsclick-udp-client-server-csma.cc and nsclick-udp-client-server-wifi.cc: A 3 node LAN (Csma and Wifi respectively) wherein 2 Click based nodes run a UDP client, that sends packets to a third Click based node running a UDP server.
  • nsclick-routing.cc: One Click based node communicates to another via a third node that acts as an IP router (using the IP router Click configuration). This demonstrates routing using Click.

Scripts are available within <click-dir>/conf/ that allow you to generate Click files for some common scenarios. The IP Router used in nsclick-routing.cc was generated from the make-ip-conf.pl file and slightly adapted to work with ns-3-click.

Validation

This model has been tested as follows:

  • Unit tests have been written to verify the internals of Ipv4ClickRouting. This can be found in src/click/ipv4-click-routing-test.cc. These tests verify whether the methods inside Ipv4ClickRouting which deal with Device name to ID, IP Address from device name and Mac Address from device name bindings work as expected.
  • The examples have been used to test Click with actual simulation scenarios. These can be found in src/click/examples/. These tests cover the following: the use of different kinds of transports on top of Click, TCP/UDP, whether Click nodes can communicate with non-Click based nodes, whether Click nodes can communicate with each other, using Click to route packets using static routing.
  • Click has been tested with Csma, Wifi and Point-to-Point devices. Usage instructions are available in the preceding section.

CSMA NetDevice

This is the introduction to CSMA NetDevice chapter, to complement the Csma model doxygen.

Overview of the CSMA model

The ns-3 CSMA device models a simple bus network in the spirit of Ethernet. Although it does not model any real physical network you could ever build or buy, it does provide some very useful functionality.

Typically when one thinks of a bus network Ethernet or IEEE 802.3 comes to mind. Ethernet uses CSMA/CD (Carrier Sense Multiple Access with Collision Detection with exponentially increasing backoff to contend for the shared transmission medium. The ns-3 CSMA device models only a portion of this process, using the nature of the globally available channel to provide instantaneous (faster than light) carrier sense and priority-based collision “avoidance.” Collisions in the sense of Ethernet never happen and so the ns-3 CSMA device does not model collision detection, nor will any transmission in progress be “jammed.”

CSMA Layer Model

There are a number of conventions in use for describing layered communications architectures in the literature and in textbooks. The most common layering model is the ISO seven layer reference model. In this view the CsmaNetDevice and CsmaChannel pair occupies the lowest two layers – at the physical (layer one), and data link (layer two) positions. Another important reference model is that specified by RFC 1122, “Requirements for Internet Hosts – Communication Layers.” In this view the CsmaNetDevice and CsmaChannel pair occupies the lowest layer – the link layer. There is also a seemingly endless litany of alternative descriptions found in textbooks and in the literature. We adopt the naming conventions used in the IEEE 802 standards which speak of LLC, MAC, MII and PHY layering. These acronyms are defined as:

  • LLC: Logical Link Control;
  • MAC: Media Access Control;
  • MII: Media Independent Interface;
  • PHY: Physical Layer.

In this case the LLC and MAC are sublayers of the OSI data link layer and the MII and PHY are sublayers of the OSI physical layer.

The “top” of the CSMA device defines the transition from the network layer to the data link layer. This transition is performed by higher layers by calling either CsmaNetDevice::Send or CsmaNetDevice::SendFrom.

In contrast to the IEEE 802.3 standards, there is no precisely specified PHY in the CSMA model in the sense of wire types, signals or pinouts. The “bottom” interface of the CsmaNetDevice can be thought of as as a kind of Media Independent Interface (MII) as seen in the “Fast Ethernet” (IEEE 802.3u) specifications. This MII interface fits into a corresponding media independent interface on the CsmaChannel. You will not find the equivalent of a 10BASE-T or a 1000BASE-LX PHY.

The CsmaNetDevice calls the CsmaChannel through a media independent interface. There is a method defined to tell the channel when to start “wiggling the wires” using the method CsmaChannel::TransmitStart, and a method to tell the channel when the transmission process is done and the channel should begin propagating the last bit across the “wire”: CsmaChannel::TransmitEnd.

When the TransmitEnd method is executed, the channel will model a single uniform signal propagation delay in the medium and deliver copes of the packet to each of the devices attached to the packet via the CsmaNetDevice::Receive method.

There is a “pin” in the device media independent interface corresponding to “COL” (collision). The state of the channel may be sensed by calling CsmaChannel::GetState. Each device will look at this “pin” before starting a send and will perform appropriate backoff operations if required.

Properly received packets are forwarded up to higher levels from the CsmaNetDevice via a callback mechanism. The callback function is initialized by the higher layer (when the net device is attached) using CsmaNetDevice::SetReceiveCallback and is invoked upon “proper” reception of a packet by the net device in order to forward the packet up the protocol stack.

CSMA Channel Model

The class CsmaChannel models the actual transmission medium. There is no fixed limit for the number of devices connected to the channel. The CsmaChannel models a data rate and a speed-of-light delay which can be accessed via the attributes “DataRate” and “Delay” respectively. The data rate provided to the channel is used to set the data rates used by the transmitter sections of the CSMA devices connected to the channel. There is no way to independently set data rates in the devices. Since the data rate is only used to calculate a delay time, there is no limitation (other than by the data type holding the value) on the speed at which CSMA channels and devices can operate; and no restriction based on any kind of PHY characteristics.

The CsmaChannel has three states, IDLE, TRANSMITTING and PROPAGATING. These three states are “seen” instantaneously by all devices on the channel. By this we mean that if one device begins or ends a simulated transmission, all devices on the channel are immediately aware of the change in state. There is no time during which one device may see an IDLE channel while another device physically further away in the collision domain may have begun transmitting with the associated signals not propagated down the channel to other devices. Thus there is no need for collision detection in the CsmaChannel model and it is not implemented in any way.

We do, as the name indicates, have a Carrier Sense aspect to the model. Since the simulator is single threaded, access to the common channel will be serialized by the simulator. This provides a deterministic mechanism for contending for the channel. The channel is allocated (transitioned from state IDLE to state TRANSMITTING) on a first-come first-served basis. The channel always goes through a three state process:

IDLE -> TRANSMITTING -> PROPAGATING -> IDLE

The TRANSMITTING state models the time during which the source net device is actually wiggling the signals on the wire. The PROPAGATING state models the time after the last bit was sent, when the signal is propagating down the wire to the “far end.”

The transition to the TRANSMITTING state is driven by a call to CsmaChannel::TransmitStart which is called by the net device that transmits the packet. It is the responsibility of that device to end the transmission with a call to CsmaChannel::TransmitEnd at the appropriate simulation time that reflects the time elapsed to put all of the packet bits on the wire. When TransmitEnd is called, the channel schedules an event corresponding to a single speed-of-light delay. This delay applies to all net devices on the channel identically. You can think of a symmetrical hub in which the packet bits propagate to a central location and then back out equal length cables to the other devices on the channel. The single “speed of light” delay then corresponds to the time it takes for: 1) a signal to propagate from one CsmaNetDevice through its cable to the hub; plus 2) the time it takes for the hub to forward the packet out a port; plus 3) the time it takes for the signal in question to propagate to the destination net device.

The CsmaChannel models a broadcast medium so the packet is delivered to all of the devices on the channel (including the source) at the end of the propagation time. It is the responsibility of the sending device to determine whether or not it receives a packet broadcast over the channel.

The CsmaChannel provides following Attributes:

  • DataRate: The bitrate for packet transmission on connected devices;
  • Delay: The speed of light transmission delay for the channel.

CSMA Net Device Model

The CSMA network device appears somewhat like an Ethernet device. The CsmaNetDevice provides following Attributes:

  • Address: The Mac48Address of the device;
  • SendEnable: Enable packet transmission if true;
  • ReceiveEnable: Enable packet reception if true;
  • EncapsulationMode: Type of link layer encapsulation to use;
  • RxErrorModel: The receive error model;
  • TxQueue: The transmit queue used by the device;
  • InterframeGap: The optional time to wait between “frames”;
  • Rx: A trace source for received packets;
  • Drop: A trace source for dropped packets.

The CsmaNetDevice supports the assignment of a “receive error model.” This is an ErrorModel object that is used to simulate data corruption on the link.

Packets sent over the CsmaNetDevice are always routed through the transmit queue to provide a trace hook for packets sent out over the network. This transmit queue can be set (via attribute) to model different queuing strategies.

Also configurable by attribute is the encapsulation method used by the device. Every packet gets an EthernetHeader that includes the destination and source MAC addresses, and a length/type field. Every packet also gets an EthernetTrailer which includes the FCS. Data in the packet may be encapsulated in different ways.

By default, or by setting the “EncapsulationMode” attribute to “Dix”, the encapsulation is according to the DEC, Intel, Xerox standard. This is sometimes called EthernetII framing and is the familiar destination MAC, source MAC, EtherType, Data, CRC format.

If the “EncapsulationMode” attribute is set to “Llc”, the encapsulation is by LLC SNAP. In this case, a SNAP header is added that contains the EtherType (IP or ARP).

The other implemented encapsulation modes are IP_ARP (set “EncapsulationMode” to “IpArp”) in which the length type of the Ethernet header receives the protocol number of the packet; or ETHERNET_V1 (set “EncapsulationMode” to “EthernetV1”) in which the length type of the Ethernet header receives the length of the packet. A “Raw” encapsulation mode is defined but not implemented – use of the RAW mode results in an assertion.

Note that all net devices on a channel must be set to the same encapsulation mode for correct results. The encapsulation mode is not sensed at the receiver.

The CsmaNetDevice implements a random exponential backoff algorithm that is executed if the channel is determined to be busy (TRANSMITTING or PPROPAGATING) when the device wants to start propagating. This results in a random delay of up to pow (2, retries) - 1 microseconds before a retry is attempted. The default maximum number of retries is 1000.

Using the CsmaNetDevice

The CSMA net devices and channels are typically created and configured using the associated CsmaHelper object. The various ns-3 device helpers generally work in a similar way, and their use is seen in many of our example programs.

The conceptual model of interest is that of a bare computer “husk” into which you plug net devices. The bare computers are created using a NodeContainer helper. You just ask this helper to create as many computers (we call them Nodes) as you need on your network:

NodeContainer csmaNodes;
csmaNodes.Create (nCsmaNodes);

Once you have your nodes, you need to instantiate a CsmaHelper and set any attributes you may want to change.:

CsmaHelper csma;
csma.SetChannelAttribute ("DataRate", StringValue ("100Mbps"));
csma.SetChannelAttribute ("Delay", TimeValue (NanoSeconds (6560)));

csma.SetDeviceAttribute ("EncapsulationMode", StringValue ("Dix"));
csma.SetDeviceAttribute ("FrameSize", UintegerValue (2000));

Once the attributes are set, all that remains is to create the devices and install them on the required nodes, and to connect the devices together using a CSMA channel. When we create the net devices, we add them to a container to allow you to use them in the future. This all takes just one line of code.:

NetDeviceContainer csmaDevices = csma.Install (csmaNodes);

We recommend thinking carefully about changing these Attributes, since it can result in behavior that surprises users. We allow this because we believe flexibility is important. As an example of a possibly surprising effect of changing Attributes, consider the following:

The Mtu Attribute indicates the Maximum Transmission Unit to the device. This is the size of the largest Protocol Data Unit (PDU) that the device can send. This Attribute defaults to 1500 bytes and corresponds to a number found in RFC 894, “A Standard for the Transmission of IP Datagrams over Ethernet Networks.” The number is actually derived from the maximum packet size for 10Base5 (full-spec Ethernet) networks – 1518 bytes. If you subtract DIX encapsulation overhead for Ethernet packets (18 bytes) you will end up with a maximum possible data size (MTU) of 1500 bytes. One can also find that the MTU for IEEE 802.3 networks is 1492 bytes. This is because LLC/SNAP encapsulation adds an extra eight bytes of overhead to the packet. In both cases, the underlying network hardware is limited to 1518 bytes, but the MTU is different because the encapsulation is different.

If one leaves the Mtu Attribute at 1500 bytes and changes the encapsulation mode Attribute to Llc, the result will be a network that encapsulates 1500 byte PDUs with LLC/SNAP framing resulting in packets of 1526 bytes. This would be illegal in many networks, but we allow you do do this. This results in a simulation that quite subtly does not reflect what you might be expecting since a real device would balk at sending a 1526 byte packet.

There also exist jumbo frames (1500 < MTU <= 9000 bytes) and super-jumbo (MTU > 9000 bytes) frames that are not officially sanctioned by IEEE but are available in some high-speed (Gigabit) networks and NICs. In the CSMA model, one could leave the encapsulation mode set to Dix, and set the Mtu to 64000 bytes – even though an associated CsmaChannel DataRate was left at 10 megabits per second (certainly not Gigabit Ethernet). This would essentially model an Ethernet switch made out of vampire-tapped 1980s-style 10Base5 networks that support super-jumbo datagrams, which is certainly not something that was ever made, nor is likely to ever be made; however it is quite easy for you to configure.

Be careful about assumptions regarding what CSMA is actually modelling and how configuration (Attributes) may allow you to swerve considerably away from reality.

CSMA Tracing

Like all ns-3 devices, the CSMA Model provides a number of trace sources. These trace sources can be hooked using your own custom trace code, or you can use our helper functions to arrange for tracing to be enabled on devices you specify.

Upper-Level (MAC) Hooks

From the point of view of tracing in the net device, there are several interesting points to insert trace hooks. A convention inherited from other simulators is that packets destined for transmission onto attached networks pass through a single “transmit queue” in the net device. We provide trace hooks at this point in packet flow, which corresponds (abstractly) only to a transition from the network to data link layer, and call them collectively the device MAC hooks.

When a packet is sent to the CSMA net device for transmission it always passes through the transmit queue. The transmit queue in the CsmaNetDevice inherits from Queue, and therefore inherits three trace sources:

  • An Enqueue operation source (see Queue::m_traceEnqueue);
  • A Dequeue operation source (see Queue::m_traceDequeue);
  • A Drop operation source (see Queue::m_traceDrop).

The upper-level (MAC) trace hooks for the CsmaNetDevice are, in fact, exactly these three trace sources on the single transmit queue of the device.

The m_traceEnqueue event is triggered when a packet is placed on the transmit queue. This happens at the time that CsmaNetDevice::Send or CsmaNetDevice::SendFrom is called by a higher layer to queue a packet for transmission.

The m_traceDequeue event is triggered when a packet is removed from the transmit queue. Dequeues from the transmit queue can happen in three situations: 1) If the underlying channel is idle when the CsmaNetDevice::Send or CsmaNetDevice::SendFrom is called, a packet is dequeued from the transmit queue and immediately transmitted; 2) If the underlying channel is idle, a packet may be dequeued and immediately transmitted in an internal TransmitCompleteEvent that functions much like a transmit complete interrupt service routine; or 3) from the random exponential backoff handler if a timeout is detected.

Case (3) implies that a packet is dequeued from the transmit queue if it is unable to be transmitted according to the backoff rules. It is important to understand that this will appear as a Dequeued packet and it is easy to incorrectly assume that the packet was transmitted since it passed through the transmit queue. In fact, a packet is actually dropped by the net device in this case. The reason for this behavior is due to the definition of the Queue Drop event. The m_traceDrop event is, by definition, fired when a packet cannot be enqueued on the transmit queue because it is full. This event only fires if the queue is full and we do not overload this event to indicate that the CsmaChannel is “full.”

Lower-Level (PHY) Hooks

Similar to the upper level trace hooks, there are trace hooks available at the lower levels of the net device. We call these the PHY hooks. These events fire from the device methods that talk directly to the CsmaChannel.

The trace source m_dropTrace is called to indicate a packet that is dropped by the device. This happens in two cases: First, if the receive side of the net device is not enabled (see CsmaNetDevice::m_receiveEnable and the associated attribute “ReceiveEnable”).

The m_dropTrace is also used to indicate that a packet was discarded as corrupt if a receive error model is used (see CsmaNetDevice::m_receiveErrorModel and the associated attribute “ReceiveErrorModel”).

The other low-level trace source fires on reception of an accepted packet (see CsmaNetDevice::m_rxTrace). A packet is accepted if it is destined for the broadcast address, a multicast address, or to the MAC address assigned to the net device.

Summary

The ns3 CSMA model is a simplistic model of an Ethernet-like network. It supports a Carrier-Sense function and allows for Multiple Access to a shared medium. It is not physical in the sense that the state of the medium is instantaneously shared among all devices. This means that there is no collision detection required in this model and none is implemented. There will never be a “jam” of a packet already on the medium. Access to the shared channel is on a first-come first-served basis as determined by the simulator scheduler. If the channel is determined to be busy by looking at the global state, a random exponential backoff is performed and a retry is attempted.

Ns-3 Attributes provide a mechanism for setting various parameters in the device and channel such as addresses, encapsulation modes and error model selection. Trace hooks are provided in the usual manner with a set of upper level hooks corresponding to a transmit queue and used in ASCII tracing; and also a set of lower level hooks used in pcap tracing.

Although the ns-3 CsmaChannel and CsmaNetDevice does not model any kind of network you could build or buy, it does provide us with some useful functionality. You should, however, understand that it is explicitly not Ethernet or any flavor of IEEE 802.3 but an interesting subset.

Data Collection

This chapter describes the ns-3 Data Collection Framework (DCF), which provides capabilities to obtain data generated by models in the simulator, to perform on-line reduction and data processing, and to marshal raw or transformed data into various output formats.

The framework presently supports standalone ns-3 runs that don’t rely on any external program execution control. The objects provided by the DCF may be hooked to ns-3 trace sources to enable data processing.

The source code for the classes lives in the directory src/stats.

This chapter is organized as follows. First, an overview of the architecture is presented. Next, the helpers for these classes are presented; this initial treatment should allow basic use of the data collection framework for many use cases. Users who wish to produce output outside of the scope of the current helpers, or who wish to create their own data collection objects, should read the remainder of the chapter, which goes into detail about all of the basic DCF object types and provides low-level coding examples.

Design

The DCF consists of three basic classes:

  • Probe is a mechanism to instrument and control the output of simulation data that is used to monitor interesting events. It produces output in the form of one or more ns-3 trace sources. Probe objects are hooked up to one or more trace sinks (called Collectors), which process samples on-line and prepare them for output.
  • Collector consumes the data generated by one or more Probe objects. It performs transformations on the data, such as normalization, reduction, and the computation of basic statistics. Collector objects do not produce data that is directly output by the ns-3 run; instead, they output data downstream to another type of object, called Aggregator, which performs that function. Typically, Collectors output their data in the form of trace sources as well, allowing collectors to be chained in series.
  • Aggregator is the end point of the data collected by a network of Probes and Collectors. The main responsibility of the Aggregator is to marshal data and their corresponding metadata, into different output formats such as plain text files, spreadsheet files, or databases.

All three of these classes provide the capability to dynamically turn themselves on or off throughout a simulation.

Any standalone ns-3 simulation run that uses the DCF will typically create at least one instance of each of the three classes above.

_images/dcf-overview.png

Data Collection Framework overview

The overall flow of data processing is depicted in Data Collection Framework overview. On the left side, a running ns-3 simulation is depicted. In the course of running the simulation, data is made available by models through trace sources, or via other means. The diagram depicts that probes can be connected to these trace sources to receive data asynchronously, or probes can poll for data. Data is then passed to a collector object that transforms the data. Finally, an aggregator can be connected to the outputs of the collector, to generate plots, files, or databases.

_images/dcf-overview-with-aggregation.png

Data Collection Framework aggregation

A variation on the above figure is provided in Data Collection Framework aggregation. This second figure illustrates that the DCF objects may be chained together in a manner that downstream objects take inputs from multiple upstream objects. The figure conceptually shows that multiple probes may generate output that is fed into a single collector; as an example, a collector that outputs a ratio of two counters would typically acquire each counter data from separate probes. Multiple collectors can also feed into a single aggregator, which (as its name implies) may collect a number of data streams for inclusion into a single plot, file, or database.

Data Collection Helpers

The full flexibility of the data collection framework is provided by the interconnection of probes, collectors, and aggregators. Performing all of these interconnections leads to many configuration statements in user programs. For ease of use, some of the most common operations can be combined and encapsulated in helper functions. In addition, some statements involving ns-3 trace sources do not have Python bindings, due to limitations in the bindings.

Data Collection Helpers Overview

In this section, we provide an overview of some helper classes that have been created to ease the configuration of the data collection framework for some common use cases. The helpers allow users to form common operations with only a few statements in their C++ or Python programs. But, this ease of use comes at the cost of significantly less flexibility than low-level configuration can provide, and the need to explicitly code support for new Probe types into the helpers (to work around an issue described below).

The emphasis on the current helpers is to marshal data out of ns-3 trace sources into gnuplot plots or text files, without a high degree of output customization or statistical processing (initially). Also, the use is constrained to the available probe types in ns-3. Later sections of this documentation will go into more detail about creating new Probe types, as well as details about hooking together Probes, Collectors, and Aggregators in custom arrangements.

To date, two Data Collection helpers have been implemented:

  • GnuplotHelper
  • FileHelper

GnuplotHelper

The GnuplotHelper is a helper class for producing output files used to make gnuplots. The overall goal is to provide the ability for users to quickly make plots from data exported in ns-3 trace sources. By default, a minimal amount of data transformation is performed; the objective is to generate plots with as few (default) configuration statements as possible.

GnuplotHelper Overview

The GnuplotHelper will create 3 different files at the end of the simulation:

  • A space separated gnuplot data file
  • A gnuplot control file
  • A shell script to generate the gnuplot

There are two configuration statements that are needed to produce plots. The first statement configures the plot (filename, title, legends, and output type, where the output type defaults to PNG if unspecified):

void ConfigurePlot (const std::string &outputFileNameWithoutExtension,
                    const std::string &title,
                    const std::string &xLegend,
                    const std::string &yLegend,
                    const std::string &terminalType = ".png");

The second statement hooks the Probe of interest:

void PlotProbe (const std::string &typeId,
                const std::string &path,
                const std::string &probeTraceSource,
                const std::string &title);

The arguments are as follows:

  • typeId: The ns-3 TypeId of the Probe
  • path: The path in the ns-3 configuration namespace to one or more probes
  • probeTraceSource: Which output of the probe should be connected to
  • title: The title to associate with the dataset (in the gnuplot legend)

A variant on the PlotProbe above is to specify a fifth optional argument that controls where in the plot the key (legend) is placed.

A fully worked example (from seventh.cc) is shown below:

// Create the gnuplot helper.
GnuplotHelper plotHelper;

// Configure the plot.
plotHelper.ConfigurePlot ("seventh-packet-byte-count",
                          "Packet Byte Count vs. Time",
                          "Time (Seconds)",
                          "Packet Byte Count",
                          "png");

// Plot the values generated by the probe.
plotHelper.PlotProbe ("ns3::Ipv4PacketProbe",
                      "/NodeList/*/$ns3::Ipv4L3Protocol/Tx",
                      "OutputBytes",
                      "Packet Byte Count",
                      GnuplotAggregator::KEY_BELOW);

Note that the path specified may contain wildcards. In this case, multiple datasets are plotted on one plot; one for each matched path.

The main output produced will be three files:

seventh-packet-byte-count.dat
seventh-packet-byte-count.plt
seventh-packet-byte-count.sh

At this point, users can either hand edit the .plt file for further customizations, or just run it through gnuplot. Running sh seventh-packet-byte-count.sh simply runs the plot through gnuplot, as shown below.

_images/seventh-packet-byte-count.png

2-D Gnuplot Created by seventh.cc Example.

It can be seen that the key elements (legend, title, legend placement, xlabel, ylabel, and path for the data) are all placed on the plot. Since there were two matches to the configuration path provided, the two data series are shown:

  • Packet Byte Count-0 corresponds to /NodeList/0/$ns3::Ipv4L3Protocol/Tx
  • Packet Byte Count-1 corresponds to /NodeList/1/$ns3::Ipv4L3Protocol/Tx
GnuplotHelper ConfigurePlot

The GnuplotHelper’s ConfigurePlot() function can be used to configure plots.

It has the following prototype:

void ConfigurePlot (const std::string &outputFileNameWithoutExtension,
                    const std::string &title,
                    const std::string &xLegend,
                    const std::string &yLegend,
                    const std::string &terminalType = ".png");

It has the following arguments:

Argument Description
outputFileNameWithoutExtension Name of gnuplot related files to write with no extension.
title Plot title string to use for this plot.
xLegend The legend for the x horizontal axis.
yLegend The legend for the y vertical axis.
terminalType Terminal type setting string for output. The default terminal type is “png”.

The GnuplotHelper’s ConfigurePlot() function configures plot related parameters for this gnuplot helper so that it will create a space separated gnuplot data file named outputFileNameWithoutExtension + ”.dat”, a gnuplot control file named outputFileNameWithoutExtension + ”.plt”, and a shell script to generate the gnuplot named outputFileNameWithoutExtension + ”.sh”.

An example of how to use this function can be seen in the seventh.cc code described above where it was used as follows:

plotHelper.ConfigurePlot ("seventh-packet-byte-count",
                          "Packet Byte Count vs. Time",
                          "Time (Seconds)",
                          "Packet Byte Count",
                          "png");
GnuplotHelper PlotProbe

The GnuplotHelper’s PlotProbe() function can be used to plot values generated by probes.

It has the following prototype:

void PlotProbe (const std::string &typeId,
                const std::string &path,
                const std::string &probeTraceSource,
                const std::string &title,
                enum GnuplotAggregator::KeyLocation keyLocation = GnuplotAggregator::KEY_INSIDE);

It has the following arguments:

Argument Description
typeId The type ID for the probe used when it is created.
path Config path to access the probe.
probeTraceSource The probe trace source to access.
title The title to be associated to this dataset
keyLocation The location of the key in the plot. The default location is inside.

The GnuplotHelper’s PlotProbe() function plots a dataset generated by hooking the ns-3 trace source with a probe, and then plotting the values from the probeTraceSource. The dataset will have the provided title, and will consist of the ‘newValue’ at each timestamp.

If the config path has more than one match in the system because there is a wildcard, then one dataset for each match will be plotted. The dataset titles will be suffixed with the matched characters for each of the wildcards in the config path, separated by spaces. For example, if the proposed dataset title is the string “bytes”, and there are two wildcards in the path, then dataset titles like “bytes-0 0” or “bytes-12 9” will be possible as labels for the datasets that are plotted.

An example of how to use this function can be seen in the seventh.cc code described above where it was used as follows:

plotHelper.PlotProbe ("ns3::Ipv4PacketProbe",
                      "/NodeList/*/$ns3::Ipv4L3Protocol/Tx",
                      "OutputBytes",
                      "Packet Byte Count",
                      GnuplotAggregator::KEY_BELOW);
Other Examples
Gnuplot Helper Example

A slightly simpler example than the seventh.cc example can be found in src/stats/examples/gnuplot-helper-example.cc. It is more of a toy example than seventh.cc because it has a made-up trace source created for demonstration purposes.

The following 2-D gnuplot was created using the example.

_images/gnuplot-helper-example.png

2-D Gnuplot Created by gnuplot-helper-example.cc Example.

In this example, there is an Emitter object that increments its counter at various random times and then emits the counter’s value as a trace source.

Ptr<Emitter> emitter = CreateObject<Emitter> ();
Names::Add ("/Names/Emitter", emitter);

The following code is probing the Counter exported by the emitter object. This DoubleProbe is using a path in the configuration namespace to make the connection. Note that the emitter registered itself in the configuration namespace after it was created; otherwise, the ConnectByPath would not work.

Ptr<DoubleProbe> probe = CreateObject<DoubleProbe> ();
probe->SetName ("PathProbe");
Names::Add ("/Names/Probe", probe);

// Note, no return value is checked here.
probe->ConnectByPath ("/Names/Emitter/Counter");

Note that because there are no wildcards in the path used below, only 1 datastream was drawn in the plot. This single datastream in the plot is simply labeled “Emitter Count”, with no extra suffixes like you would see if there were wildcards in the path.

// Create the gnuplot helper.
GnuplotHelper plotHelper;

// Configure the plot.
plotHelper.ConfigurePlot ("gnuplot-helper-example",
                          "Emitter Counts vs. Time",
                          "Time (Seconds)",
                          "Emitter Count",
                          "png");

// Plot the values generated by the probe.  The path that we provide
// helps to disambiguate the source of the trace.
plotHelper.PlotProbe ("ns3::DoubleProbe",
                      "/Names/Probe/Output",
                      "Output",
                      "Emitter Count",
                      GnuplotAggregator::KEY_INSIDE);

FileHelper

The FileHelper is a helper class used to put data values into a file. The overall goal is to provide the ability for users to quickly make formatted text files from data exported in ns-3 trace sources. By default, a minimal amount of data transformation is performed; the objective is to generate files with as few (default) configuration statements as possible.

FileHelper Overview

The FileHelper will create 1 or more text files at the end of the simulation.

The FileHelper can create 4 different types of text files:

  • Formatted
  • Space separated (the default)
  • Comma separated
  • Tab separated

Formatted files use C-style format strings and the sprintf() function to print their values in the file being written.

The following text file with 2 columns of formatted values named seventh-packet-byte-count-0.txt was created using more new code that was added to the original ns-3 Tutorial example’s code. Only the first 10 lines of this file are shown here for brevity.

Time (Seconds) = 1.000e+00    Packet Byte Count = 40
Time (Seconds) = 1.004e+00    Packet Byte Count = 40
Time (Seconds) = 1.004e+00    Packet Byte Count = 576
Time (Seconds) = 1.009e+00    Packet Byte Count = 576
Time (Seconds) = 1.009e+00    Packet Byte Count = 576
Time (Seconds) = 1.015e+00    Packet Byte Count = 512
Time (Seconds) = 1.017e+00    Packet Byte Count = 576
Time (Seconds) = 1.017e+00    Packet Byte Count = 544
Time (Seconds) = 1.025e+00    Packet Byte Count = 576
Time (Seconds) = 1.025e+00    Packet Byte Count = 544

...

The following different text file with 2 columns of formatted values named seventh-packet-byte-count-1.txt was also created using the same new code that was added to the original ns-3 Tutorial example’s code. Only the first 10 lines of this file are shown here for brevity.

Time (Seconds) = 1.002e+00    Packet Byte Count = 40
Time (Seconds) = 1.007e+00    Packet Byte Count = 40
Time (Seconds) = 1.013e+00    Packet Byte Count = 40
Time (Seconds) = 1.020e+00    Packet Byte Count = 40
Time (Seconds) = 1.028e+00    Packet Byte Count = 40
Time (Seconds) = 1.036e+00    Packet Byte Count = 40
Time (Seconds) = 1.045e+00    Packet Byte Count = 40
Time (Seconds) = 1.053e+00    Packet Byte Count = 40
Time (Seconds) = 1.061e+00    Packet Byte Count = 40
Time (Seconds) = 1.069e+00    Packet Byte Count = 40

...

The new code that was added to produce the two text files is below. More details about this API will be covered in a later section.

Note that because there were 2 matches for the wildcard in the path, 2 separate text files were created. The first text file, which is named “seventh-packet-byte-count-0.txt”, corresponds to the wildcard match with the “*” replaced with “0”. The second text file, which is named “seventh-packet-byte-count-1.txt”, corresponds to the wildcard match with the “*” replaced with “1”. Also, note that the function call to WriteProbe() will give an error message if there are no matches for a path that contains wildcards.

// Create the file helper.
FileHelper fileHelper;

// Configure the file to be written.
fileHelper.ConfigureFile ("seventh-packet-byte-count",
                          FileAggregator::FORMATTED);

// Set the labels for this formatted output file.
fileHelper.Set2dFormat ("Time (Seconds) = %.3e\tPacket Byte Count = %.0f");

// Write the values generated by the probe.
fileHelper.WriteProbe ("ns3::Ipv4PacketProbe",
                       "/NodeList/*/$ns3::Ipv4L3Protocol/Tx",
                       "OutputBytes");
FileHelper ConfigureFile

The FileHelper’s ConfigureFile() function can be used to configure text files.

It has the following prototype:

void ConfigureFile (const std::string &outputFileNameWithoutExtension,
                    enum FileAggregator::FileType fileType = FileAggregator::SPACE_SEPARATED);

It has the following arguments:

Argument Description
outputFileNameWithoutExtension Name of output file to write with no extension.
fileType Type of file to write. The default type of file is space separated.

The FileHelper’s ConfigureFile() function configures text file related parameters for the file helper so that it will create a file named outputFileNameWithoutExtension plus possible extra information from wildcard matches plus ”.txt” with values printed as specified by fileType. The default file type is space-separated.

An example of how to use this function can be seen in the seventh.cc code described above where it was used as follows:

fileHelper.ConfigureFile ("seventh-packet-byte-count",
                          FileAggregator::FORMATTED);
FileHelper WriteProbe

The FileHelper’s WriteProbe() function can be used to write values generated by probes to text files.

It has the following prototype:

void WriteProbe (const std::string &typeId,
                 const std::string &path,
                 const std::string &probeTraceSource);

It has the following arguments:

Argument Description
typeId The type ID for the probe used when it is created.
path Config path to access the probe.
probeTraceSource The probe trace source to access.

The FileHelper’s WriteProbe() function creates output text files generated by hooking the ns-3 trace source with a probe, and then writing the values from the probeTraceSource. The output file names will have the text stored in the member variable m_outputFileNameWithoutExtension plus ”.txt”, and will consist of the ‘newValue’ at each timestamp.

If the config path has more than one match in the system because there is a wildcard, then one output file for each match will be created. The output file names will contain the text in m_outputFileNameWithoutExtension plus the matched characters for each of the wildcards in the config path, separated by dashes, plus ”.txt”. For example, if the value in m_outputFileNameWithoutExtension is the string “packet-byte-count”, and there are two wildcards in the path, then output file names like “packet-byte-count-0-0.txt” or “packet-byte-count-12-9.txt” will be possible as names for the files that will be created.

An example of how to use this function can be seen in the seventh.cc code described above where it was used as follows:

fileHelper.WriteProbe ("ns3::Ipv4PacketProbe",
                       "/NodeList/*/$ns3::Ipv4L3Protocol/Tx",
                       "OutputBytes");
Other Examples
File Helper Example

A slightly simpler example than the seventh.cc example can be found in src/stats/examples/file-helper-example.cc. This example only uses the FileHelper, not the FileHelper. It is also more of a toy example than seventh.cc because it has a made-up trace source created for demonstration purposes.

The following text file with 2 columns of formatted values named file-helper-example.txt was created using the example. Only the first 10 lines of this file are shown here for brevity.

Time (Seconds) = 4.995e-01    Count = 1
Time (Seconds) = 1.463e+00    Count = 2
Time (Seconds) = 1.678e+00    Count = 3
Time (Seconds) = 3.972e+00    Count = 4
Time (Seconds) = 4.150e+00    Count = 5
Time (Seconds) = 8.066e+00    Count = 6
Time (Seconds) = 8.731e+00    Count = 7
Time (Seconds) = 9.807e+00    Count = 8
Time (Seconds) = 1.078e+01    Count = 9
Time (Seconds) = 1.083e+01    Count = 10

...

In this example, there is an Emitter object that increments its counter at various random times and then emits the counter’s value as a trace source.

Ptr<Emitter> emitter = CreateObject<Emitter> ();
Names::Add ("/Names/Emitter", emitter);

The following code is probing the Counter exported by the emitter object. This DoubleProbe is using a path in the configuration namespace to make the connection. Note that the emitter registered itself in the configuration namespace after it was created; otherwise, the ConnectByPath would not work.

Ptr<DoubleProbe> probe = CreateObject<DoubleProbe> ();
probe->SetName ("PathProbe");
Names::Add ("/Names/Probe", probe);

// Note, no return value is checked here.
probe->ConnectByPath ("/Names/Emitter/Counter");

Note that because there are no wildcards in the path used below, only 1 text file was created. This single text file is simply named “file-helper-example.txt”, with no extra suffixes like you would see if there were wildcards in the path.

// Create the file helper.
FileHelper fileHelper;

// Configure the file to be written.
fileHelper.ConfigureFile ("file-helper-example",
                          FileAggregator::FORMATTED);

// Set the labels for this formatted output file.
fileHelper.Set2dFormat ("Time (Seconds) = %.3e\tCount = %.0f");

// Write the values generated by the probe.  The path that we
// provide helps to disambiguate the source of the trace.
fileHelper.WriteProbe ("ns3::DoubleProbe",
                       "/Names/Probe/Output",
                       "Output");

Scope and Limitations

Currently, only these Probes have been implemented and connected to the GnuplotHelper and to the FileHelper:

  • BooleanProbe
  • DoubleProbe
  • Uinteger8Probe
  • Uinteger16Probe
  • Uinteger32Probe
  • PacketProbe
  • ApplicationPacketProbe
  • Ipv4PacketProbe

These Probes, therefore, are the only ones available to be used in PlotProbe() and WriteProbe().

In the next few sections, we cover each of the fundamental object types (Probe, Collector, and Aggregator) in more detail, and show how they can be connected together using lower-level API.

Probes

This section details the functionalities provided by the Probe class to an ns-3 simulation, and gives examples on how to code them in a program. This section is meant for users interested in developing simulations with the ns-3 tools and using the Data Collection Framework, of which the Probe class is a part, to generate data output with their simulation’s results.

Probe Overview

A Probe object is supposed to be connected to a variable from the simulation whose values throughout the experiment are relevant to the user. The Probe will record what were values assumed by the variable throughout the simulation and pass such data to another member of the Data Collection Framework. While it is out of this section’s scope to discuss what happens after the Probe produces its output, it is sufficient to say that, by the end of the simulation, the user will have detailed information about what values were stored inside the variable being probed during the simulation.

Typically, a Probe is connected to an ns-3 trace source. In this manner, whenever the trace source exports a new value, the Probe consumes the value (and exports it downstream to another object via its own trace source).

The Probe can be thought of as kind of a filter on trace sources. The main reasons for possibly hooking to a Probe rather than directly to a trace source are as follows:

  • Probes may be dynamically turned on and off during the simulation with calls to Enable() and Disable(). For example, the outputting of data may be turned off during the simulation warmup phase.

  • Probes may perform operations on the data to extract values from more complicated structures; for instance, outputting the packet size value from a received ns3::Packet.

  • Probes register a name in the ns3::Config namespace (using Names::Add ()) so that other objects may refer to them.

  • Probes provide a static method that allows one to manipulate a Probe by name, such as what is done in ns2measure [Cic06]

    Stat::put ("my_metric", ID, sample);
    

    The ns-3 equivalent of the above ns2measure code is, e.g.

    DoubleProbe::SetValueByPath ("/path/to/probe", sample);
    
Creation

Note that a Probe base class object can not be created because it is an abstract base class, i.e. it has pure virtual functions that have not been implemented. An object of type DoubleProbe, which is a subclass of the Probe class, will be created here to show what needs to be done.

One declares a DoubleProbe in dynamic memory by using the smart pointer class (Ptr<T>). To create a DoubleProbe in dynamic memory with smart pointers, one just needs to call the ns-3 method CreateObject():

Ptr<DoubleProbe> myprobe = CreateObject<DoubleProbe> ();

The declaration above creates DoubleProbes using the default values for its attributes. There are four attributes in the DoubleProbe class; two in the base class object DataCollectionObject, and two in the Probe base class:

  • “Name” (DataCollectionObject), a StringValue
  • “Enabled” (DataCollectionObject), a BooleanValue
  • “Start” (Probe), a TimeValue
  • “Stop” (Probe), a TimeValue

One can set such attributes at object creation by using the following method:

Ptr<DoubleProbe> myprobe = CreateObjectWithAttributes<DoubleProbe> (
    "Name", StringValue ("myprobe"),
    "Enabled", BooleanValue (false),
    "Start", TimeValue (Seconds (100.0)),
    "Stop", TimeValue (Seconds (1000.0)));

Start and Stop are Time variables which determine the interval of action of the Probe. The Probe will only output data if the current time of the Simulation is inside of that interval. The special time value of 0 seconds for Stop will disable this attribute (i.e. keep the Probe on for the whole simulation). Enabled is a flag that turns the Probe on or off, and must be set to true for the Probe to export data. The Name is the object’s name in the DCF framework.

Importing and exporting data

ns-3 trace sources are strongly typed, so the mechanisms for hooking Probes to a trace source and for exporting data belong to its subclasses. For instance, the default distribution of ns-3 provides a class DoubleProbe that is designed to hook to a trace source exporting a double value. We’ll next detail the operation of the DoubleProbe, and then discuss how other Probe classes may be defined by the user.

DoubleProbe Overview

The DoubleProbe connects to a double-valued ns-3 trace source, and itself exports a different double-valued ns-3 trace source.

The following code, drawn from src/stats/examples/double-probe-example.cc, shows the basic operations of plumbing the DoubleProbe into a simulation, where it is probing a Counter exported by an emitter object (class Emitter).

Ptr<Emitter> emitter = CreateObject<Emitter> ();
Names::Add ("/Names/Emitter", emitter);
...

Ptr<DoubleProbe> probe1 = CreateObject<DoubleProbe> ();

// Connect the probe to the emitter's Counter
bool connected = probe1->ConnectByObject ("Counter", emitter);

The following code is probing the same Counter exported by the same emitter object. This DoubleProbe, however, is using a path in the configuration namespace to make the connection. Note that the emitter registered itself in the configuration namespace after it was created; otherwise, the ConnectByPath would not work.

Ptr<DoubleProbe> probe2 = CreateObject<DoubleProbe> ();

// Note, no return value is checked here
probe2->ConnectByPath ("/Names/Emitter/Counter");

The next DoubleProbe shown that is shown below will have its value set using its path in the configuration namespace. Note that this time the DoubleProbe registered itself in the configuration namespace after it was created.

Ptr<DoubleProbe> probe3 = CreateObject<DoubleProbe> ();
probe3->SetName ("StaticallyAccessedProbe");

// We must add it to the config database
Names::Add ("/Names/Probes", probe3->GetName (), probe3);

The emitter’s Count() function is now able to set the value for this DoubleProbe as follows:

void
Emitter::Count (void)
{
  ...
  m_counter += 1.0;
  DoubleProbe::SetValueByPath ("/Names/StaticallyAccessedProbe", m_counter);
  ...
}

The above example shows how the code calling the Probe does not have to have an explicit reference to the Probe, but can direct the value setting through the Config namespace. This is similar in functionality to the Stat::Put method introduced by ns2measure paper [Cic06], and allows users to temporarily insert Probe statements like printf statements within existing ns-3 models. Note that in order to be able to use the DoubleProbe in this example like this, 2 things were necessary:

  1. the stats module header file was included in the example .cc file
  2. the example was made dependent on the stats module in its wscript file.

Analogous things need to be done in order to add other Probes in other places in the ns-3 code base.

The values for the DoubleProbe can also be set using the function DoubleProbe::SetValue(), while the values for the DoubleProbe can be gotten using the function DoubleProbe::GetValue().

The DoubleProbe exports double values in its “Output” trace source; a downstream object can hook a trace sink (NotifyViaProbe) to this as follows:

connected = probe1->TraceConnect ("Output", probe1->GetName (), MakeCallback (&NotifyViaProbe));

Other probes

Besides the DoubleProbe, the following Probes are also available:

  • Uinteger8Probe connects to an ns-3 trace source exporting an uint8_t.
  • Uinteger16Probe connects to an ns-3 trace source exporting an uint16_t.
  • Uinteger32Probe connects to an ns-3 trace source exporting an uint32_t.
  • PacketProbe connects to an ns-3 trace source exporting a packet.
  • ApplicationPacketProbe connects to an ns-3 trace source exporting a packet and a socket address.
  • Ipv4PacketProbe connects to an ns-3 trace source exporting a packet, an IPv4 object, and an interface.

Creating new Probe types

To create a new Probe type, you need to perform the following steps:

  • Be sure that your new Probe class is derived from the Probe base class.
  • Be sure that the pure virtual functions that your new Probe class inherits from the Probe base class are implemented.
  • Find an existing Probe class that uses a trace source that is closest in type to the type of trace source your Probe will be using.
  • Copy that existing Probe class’s header file (.h) and implementation file (.cc) to two new files with names matching your new Probe.
  • Replace the types, arguments, and variables in the copied files with the appropriate type for your Probe.
  • Make necessary modifications to make the code compile and to make it behave as you would like.

Examples

Two examples will be discussed in detail here:

  • Double Probe Example
  • IPv4 Packet Plot Example
Double Probe Example

The double probe example has been discussed previously. The example program can be found in src/stats/examples/double-probe-example.cc. To summarize what occurs in this program, there is an emitter that exports a counter that increments according to a Poisson process. In particular, two ways of emitting data are shown:

  1. through a traced variable hooked to one Probe:

    TracedValue<double> m_counter;  // normally this would be integer type
    
  2. through a counter whose value is posted to a second Probe, referenced by its name in the Config system:

void
Emitter::Count (void)
{
  NS_LOG_FUNCTION (this);
  NS_LOG_DEBUG ("Counting at " << Simulator::Now ().GetSeconds ());
  m_counter += 1.0;
  DoubleProbe::SetValueByPath ("/Names/StaticallyAccessedProbe", m_counter);
  Simulator::Schedule (Seconds (m_var->GetValue ()), &Emitter::Count, this);
}

Let’s look at the Probe more carefully. Probes can receive their values in a multiple ways:

  1. by the Probe accessing the trace source directly and connecting a trace sink to it
  2. by the Probe accessing the trace source through the config namespace and connecting a trace sink to it
  3. by the calling code explicitly calling the Probe’s SetValue() method
  4. by the calling code explicitly calling SetValueByPath (“/path/through/Config/namespace”, ...)

The first two techniques are expected to be the most common. Also in the example, the hooking of a normal callback function is shown, as is typically done in ns-3. This callback function is not associated with a Probe object. We’ll call this case 0) below.

// This is a function to test hooking a raw function to the trace source
void
NotifyViaTraceSource (std::string context, double oldVal, double newVal)
{
  NS_LOG_DEBUG ("context: " << context << " old " << oldVal << " new " << newVal);
}

First, the emitter needs to be setup:

Ptr<Emitter> emitter = CreateObject<Emitter> ();
Names::Add ("/Names/Emitter", emitter);

// The Emitter object is not associated with an ns-3 node, so
// it won't get started automatically, so we need to do this ourselves
Simulator::Schedule (Seconds (0.0), &Emitter::Start, emitter);

The various DoubleProbes interact with the emitter in the example as shown below.

Case 0):

// The below shows typical functionality without a probe
// (connect a sink function to a trace source)
//
connected = emitter->TraceConnect ("Counter", "sample context", MakeCallback (&NotifyViaTraceSource));
NS_ASSERT_MSG (connected, "Trace source not connected");

case 1):

//
// Probe1 will be hooked directly to the Emitter trace source object
//

// probe1 will be hooked to the Emitter trace source
Ptr<DoubleProbe> probe1 = CreateObject<DoubleProbe> ();
// the probe's name can serve as its context in the tracing
probe1->SetName ("ObjectProbe");

// Connect the probe to the emitter's Counter
connected = probe1->ConnectByObject ("Counter", emitter);
NS_ASSERT_MSG (connected, "Trace source not connected to probe1");

case 2):

//
// Probe2 will be hooked to the Emitter trace source object by
// accessing it by path name in the Config database
//

// Create another similar probe; this will hook up via a Config path
Ptr<DoubleProbe> probe2 = CreateObject<DoubleProbe> ();
probe2->SetName ("PathProbe");

// Note, no return value is checked here
probe2->ConnectByPath ("/Names/Emitter/Counter");

case 4) (case 3 is not shown in this example):

//
// Probe3 will be called by the emitter directly through the
// static method SetValueByPath().
//
Ptr<DoubleProbe> probe3 = CreateObject<DoubleProbe> ();
probe3->SetName ("StaticallyAccessedProbe");
// We must add it to the config database
Names::Add ("/Names/Probes", probe3->GetName (), probe3);

And finally, the example shows how the probes can be hooked to generate output:

// The probe itself should generate output.  The context that we provide
// to this probe (in this case, the probe name) will help to disambiguate
// the source of the trace
connected = probe3->TraceConnect ("Output",
                                  "/Names/Probes/StaticallyAccessedProbe/Output",
                                  MakeCallback (&NotifyViaProbe));
NS_ASSERT_MSG (connected, "Trace source not .. connected to probe3 Output");

The following callback is hooked to the Probe in this example for illustrative purposes; normally, the Probe would be hooked to a Collector object.

// This is a function to test hooking it to the probe output
void
NotifyViaProbe (std::string context, double oldVal, double newVal)
{
  NS_LOG_DEBUG ("context: " << context << " old " << oldVal << " new " << newVal);
}
IPv4 Packet Plot Example

The IPv4 packet plot example is based on the fifth.cc example from the ns-3 Tutorial. It can be found in src/stats/examples/ipv4-packet-plot-example.cc.

      node 0                 node 1
+----------------+    +----------------+
|    ns-3 TCP    |    |    ns-3 TCP    |
+----------------+    +----------------+
|    10.1.1.1    |    |    10.1.1.2    |
+----------------+    +----------------+
| point-to-point |    | point-to-point |
+----------------+    +----------------+
        |                     |
        +---------------------+

We’ll just look at the Probe, as it illustrates that Probes may also unpack values from structures (in this case, packets) and report those values as trace source outputs, rather than just passing through the same type of data.

There are other aspects of this example that will be explained later in the documentation. The two types of data that are exported are the packet itself (Output) and a count of the number of bytes in the packet (OutputBytes).

TypeId
Ipv4PacketProbe::GetTypeId ()
{
  static TypeId tid = TypeId ("ns3::Ipv4PacketProbe")
    .SetParent<Probe> ()
    .AddConstructor<Ipv4PacketProbe> ()
    .AddTraceSource ( "Output",
                      "The packet plus its IPv4 object and interface that serve as the output for this probe",
                      MakeTraceSourceAccessor (&Ipv4PacketProbe::m_output))
    .AddTraceSource ( "OutputBytes",
                      "The number of bytes in the packet",
                      MakeTraceSourceAccessor (&Ipv4PacketProbe::m_outputBytes))
  ;
  return tid;
}

When the Probe’s trace sink gets a packet, if the Probe is enabled, then it will output the packet on its Output trace source, but it will also output the number of bytes on the OutputBytes trace source.

void
Ipv4PacketProbe::TraceSink (Ptr<const Packet> packet, Ptr<Ipv4> ipv4, uint32_t interface)
{
  NS_LOG_FUNCTION (this << packet << ipv4 << interface);
  if (IsEnabled ())
    {
      m_packet    = packet;
      m_ipv4      = ipv4;
      m_interface = interface;
      m_output (packet, ipv4, interface);

      uint32_t packetSizeNew = packet->GetSize ();
      m_outputBytes (m_packetSizeOld, packetSizeNew);
      m_packetSizeOld = packetSizeNew;
    }
}

References

[Cic06](1, 2) Claudio Cicconetti, Enzo Mingozzi, Giovanni Stea, “An Integrated Framework for Enabling Effective Data Collection and Statistical Analysis with ns2, Workshop on ns-2 (WNS2), Pisa, Italy, October 2006.

Collectors

This section is a placeholder to detail the functionalities provided by the Collector class to an ns-3 simulation, and gives examples on how to code them in a program.

Note: As of ns-3.18, Collectors are still under development and not yet provided as part of the framework.

Aggregators

This section details the functionalities provided by the Aggregator class to an ns-3 simulation. This section is meant for users interested in developing simulations with the ns-3 tools and using the Data Collection Framework, of which the Aggregator class is a part, to generate data output with their simulation’s results.

Aggregator Overview

An Aggregator object is supposed to be hooked to one or more trace sources in order to receive input. Aggregators are the end point of the data collected by the network of Probes and Collectors during the simulation. It is the Aggregator’s job to take these values and transform them into their final output format such as plain text files, spreadsheet files, plots, or databases.

Typically, an aggregator is connected to one or more Collectors. In this manner, whenever the Collectors’ trace sources export new values, the Aggregator can process the value so that it can be used in the final output format where the data values will reside after the simulation.

Note the following about Aggregators:

  • Aggregators may be dynamically turned on and off during the simulation with calls to Enable() and Disable(). For example, the aggregating of data may be turned off during the simulation warmup phase, which means those values won’t be included in the final output medium.
  • Aggregators receive data from Collectors via callbacks. When a Collector is associated to an aggregator, a call to TraceConnect is made to establish the Aggregator’s trace sink method as a callback.

To date, two Aggregators have been implemented:

  • GnuplotAggregator
  • FileAggregator

GnuplotAggregator

The GnuplotAggregator produces output files used to make gnuplots.

The GnuplotAggregator will create 3 different files at the end of the simulation:

  • A space separated gnuplot data file
  • A gnuplot control file
  • A shell script to generate the gnuplot
Creation

An object of type GnuplotAggregator will be created here to show what needs to be done.

One declares a GnuplotAggregator in dynamic memory by using the smart pointer class (Ptr<T>). To create a GnuplotAggregator in dynamic memory with smart pointers, one just needs to call the ns-3 method CreateObject(). The following code from src/stats/examples/gnuplot-aggregator-example.cc shows how to do this:

string fileNameWithoutExtension = "gnuplot-aggregator";

// Create an aggregator.
Ptr<GnuplotAggregator> aggregator =
  CreateObject<GnuplotAggregator> (fileNameWithoutExtension);

The first argument for the constructor, fileNameWithoutExtension, is the name of the gnuplot related files to write with no extension. This GnuplotAggregator will create a space separated gnuplot data file named “gnuplot-aggregator.dat”, a gnuplot control file named “gnuplot-aggregator.plt”, and a shell script to generate the gnuplot named + “gnuplot-aggregator.sh”.

The gnuplot that is created can have its key in 4 different locations:

  • No key
  • Key inside the plot (the default)
  • Key above the plot
  • Key below the plot

The following gnuplot key location enum values are allowed to specify the key’s position:

enum KeyLocation {
  NO_KEY,
  KEY_INSIDE,
  KEY_ABOVE,
  KEY_BELOW
};

If it was desired to have the key below rather than the default position of inside, then you could do the following.

aggregator->SetKeyLocation(GnuplotAggregator::KEY_BELOW);
Examples

One example will be discussed in detail here:

  • Gnuplot Aggregator Example
Gnuplot Aggregator Example

An example that exercises the GnuplotAggregator can be found in src/stats/examples/gnuplot-aggregator-example.cc.

The following 2-D gnuplot was created using the example.

_images/gnuplot-aggregator.png

2-D Gnuplot Created by gnuplot-aggregator-example.cc Example.

This code from the example shows how to construct the GnuplotAggregator as was discussed above.

void Create2dPlot ()
{
  using namespace std;

  string fileNameWithoutExtension = "gnuplot-aggregator";
  string plotTitle                = "Gnuplot Aggregator Plot";
  string plotXAxisHeading         = "Time (seconds)";
  string plotYAxisHeading         = "Double Values";
  string plotDatasetLabel         = "Data Values";
  string datasetContext           = "Dataset/Context/String";

  // Create an aggregator.
  Ptr<GnuplotAggregator> aggregator =
    CreateObject<GnuplotAggregator> (fileNameWithoutExtension);

Various GnuplotAggregator attributes are set including the 2-D dataset that will be plotted.

// Set the aggregator's properties.
aggregator->SetTerminal ("png");
aggregator->SetTitle (plotTitle);
aggregator->SetLegend (plotXAxisHeading, plotYAxisHeading);

// Add a data set to the aggregator.
aggregator->Add2dDataset (datasetContext, plotDatasetLabel);

// aggregator must be turned on
aggregator->Enable ();

Next, the 2-D values are calculated, and each one is individually written to the GnuplotAggregator using the Write2d() function.

  double time;
  double value;

  // Create the 2-D dataset.
  for (time = -5.0; time <= +5.0; time += 1.0)
    {
      // Calculate the 2-D curve
      //
      //                   2
      //     value  =  time   .
      //
      value = time * time;

      // Add this point to the plot.
      aggregator->Write2d (datasetContext, time, value);
    }

  // Disable logging of data for the aggregator.
  aggregator->Disable ();
}

FileAggregator

The FileAggregator sends the values it receives to a file.

The FileAggregator can create 4 different types of files:

  • Formatted
  • Space separated (the default)
  • Comma separated
  • Tab separated

Formatted files use C-style format strings and the sprintf() function to print their values in the file being written.

Creation

An object of type FileAggregator will be created here to show what needs to be done.

One declares a FileAggregator in dynamic memory by using the smart pointer class (Ptr<T>). To create a FileAggregator in dynamic memory with smart pointers, one just needs to call the ns-3 method CreateObject. The following code from src/stats/examples/file-aggregator-example.cc shows how to do this:

string fileName       = "file-aggregator-formatted-values.txt";

// Create an aggregator that will have formatted values.
Ptr<FileAggregator> aggregator =
  CreateObject<FileAggregator> (fileName, FileAggregator::FORMATTED);

The first argument for the constructor, filename, is the name of the file to write; the second argument, fileType, is type of file to write. This FileAggregator will create a file named “file-aggregator-formatted-values.txt” with its values printed as specified by fileType, i.e., formatted in this case.

The following file type enum values are allowed:

enum FileType {
  FORMATTED,
  SPACE_SEPARATED,
  COMMA_SEPARATED,
  TAB_SEPARATED
};
Examples

One example will be discussed in detail here:

  • File Aggregator Example
File Aggregator Example

An example that exercises the FileAggregator can be found in src/stats/examples/file-aggregator-example.cc.

The following text file with 2 columns of values separated by commas was created using the example.

-5,25
-4,16
-3,9
-2,4
-1,1
0,0
1,1
2,4
3,9
4,16
5,25

This code from the example shows how to construct the FileAggregator as was discussed above.

void CreateCommaSeparatedFile ()
{
  using namespace std;

  string fileName       = "file-aggregator-comma-separated.txt";
  string datasetContext = "Dataset/Context/String";

  // Create an aggregator.
  Ptr<FileAggregator> aggregator =
    CreateObject<FileAggregator> (fileName, FileAggregator::COMMA_SEPARATED);

FileAggregator attributes are set.

// aggregator must be turned on
aggregator->Enable ();

Next, the 2-D values are calculated, and each one is individually written to the FileAggregator using the Write2d() function.

  double time;
  double value;

  // Create the 2-D dataset.
  for (time = -5.0; time <= +5.0; time += 1.0)
    {
      // Calculate the 2-D curve
      //
      //                   2
      //     value  =  time   .
      //
      value = time * time;

      // Add this point to the plot.
      aggregator->Write2d (datasetContext, time, value);
    }

  // Disable logging of data for the aggregator.
  aggregator->Disable ();
}

The following text file with 2 columns of formatted values was also created using the example.

Time = -5.000e+00     Value = 25
Time = -4.000e+00     Value = 16
Time = -3.000e+00     Value = 9
Time = -2.000e+00     Value = 4
Time = -1.000e+00     Value = 1
Time = 0.000e+00      Value = 0
Time = 1.000e+00      Value = 1
Time = 2.000e+00      Value = 4
Time = 3.000e+00      Value = 9
Time = 4.000e+00      Value = 16
Time = 5.000e+00      Value = 25

This code from the example shows how to construct the FileAggregator as was discussed above.

void CreateFormattedFile ()
{
  using namespace std;

  string fileName       = "file-aggregator-formatted-values.txt";
  string datasetContext = "Dataset/Context/String";

  // Create an aggregator that will have formatted values.
  Ptr<FileAggregator> aggregator =
    CreateObject<FileAggregator> (fileName, FileAggregator::FORMATTED);

FileAggregator attributes are set, including the C-style format string to use.

// Set the format for the values.
aggregator->Set2dFormat ("Time = %.3e\tValue = %.0f");

// aggregator must be turned on
aggregator->Enable ();

Next, the 2-D values are calculated, and each one is individually written to the FileAggregator using the Write2d() function.

  double time;
  double value;

  // Create the 2-D dataset.
  for (time = -5.0; time <= +5.0; time += 1.0)
    {
      // Calculate the 2-D curve
      //
      //                   2
      //     value  =  time   .
      //
      value = time * time;

      // Add this point to the plot.
      aggregator->Write2d (datasetContext, time, value);
    }

  // Disable logging of data for the aggregator.
  aggregator->Disable ();
}

Adaptors

This section details the functionalities provided by the Adaptor class to an ns-3 simulation. This section is meant for users interested in developing simulations with the ns-3 tools and using the Data Collection Framework, of which the Adaptor class is a part, to generate data output with their simulation’s results.

Note: the term ‘adaptor’ may also be spelled ‘adapter’; we chose the spelling aligned with the C++ standard.

Adaptor Overview

An Adaptor is used to make connections between different types of DCF objects.

To date, one Adaptor has been implemented:

  • TimeSeriesAdaptor

Time Series Adaptor

The TimeSeriesAdaptor lets Probes connect directly to Aggregators without needing any Collector in between.

Both of the implemented DCF helpers utilize TimeSeriesAdaptors in order to take probed values of different types and output the current time plus the value with both converted to doubles.

The role of the TimeSeriesAdaptor class is that of an adaptor, which takes raw-valued probe data of different types and outputs a tuple of two double values. The first is a timestamp, which may be set to different resolutions (e.g. Seconds, Milliseconds, etc.) in the future but which is presently hardcoded to Seconds. The second is the conversion of a non-double value to a double value (possibly with loss of precision).

Statistical Framework

This chapter outlines work on simulation data collection and the statistical framework for ns-3.

The source code for the statistical framework lives in the directory src/stats.

Goals

Primary objectives for this effort are the following:

  • Provide functionality to record, calculate, and present data and statistics for analysis of network simulations.
  • Boost simulation performance by reducing the need to generate extensive trace logs in order to collect data.
  • Enable simulation control via online statistics, e.g. terminating simulations or repeating trials.

Derived sub-goals and other target features include the following:

  • Integration with the existing ns-3 tracing system as the basic instrumentation framework of the internal simulation engine, e.g. network stacks, net devices, and channels.
  • Enabling users to utilize the statistics framework without requiring use of the tracing system.
  • Helping users create, aggregate, and analyze data over multiple trials.
  • Support for user created instrumentation, e.g. of application specific events and measures.
  • Low memory and CPU overhead when the package is not in use.
  • Leveraging existing analysis and output tools as much as possible. The framework may provide some basic statistics, but the focus is on collecting data and making it accessible for manipulation in established tools.
  • Eventual support for distributing independent replications is important but not included in the first round of features.

Overview

The statistics framework includes the following features:

  • The core framework and two basic data collectors: A counter, and a min/max/avg/total observer.
  • Extensions of those to easily work with times and packets.
  • Plaintext output formatted for OMNet++.
  • Database output using SQLite, a standalone, lightweight, high performance SQL engine.
  • Mandatory and open ended metadata for describing and working with runs.
  • An example based on the notional experiment of examining the properties of NS-3’s default ad hoc WiFi performance. It incorporates the following:
    • Constructs of a two node ad hoc WiFi network, with the nodes a parameterized distance apart.
    • UDP traffic source and sink applications with slightly different behavior and measurement hooks than the stock classes.
    • Data collection from the NS-3 core via existing trace signals, in particular data on frames transmitted and received by the WiFi MAC objects.
    • Instrumentation of custom applications by connecting new trace signals to the stat framework, as well as via direct updates. Information is recorded about total packets sent and received, bytes transmitted, and end-to-end delay.
    • An example of using packet tags to track end-to-end delay.
    • A simple control script which runs a number of trials of the experiment at varying distances and queries the resulting database to produce a graph using GNUPlot.

To-Do

High priority items include:

  • Inclusion of online statistics code, e.g. for memory efficient confidence intervals.
  • Provisions in the data collectors for terminating runs, i.e. when a threshold or confidence is met.
  • Data collectors for logging samples over time, and output to the various formats.
  • Demonstrate writing simple cyclic event glue to regularly poll some value.

Each of those should prove straightforward to incorporate in the current framework.

Approach

The framework is based around the following core principles:

  • One experiment trial is conducted by one instance of a simulation program, whether in parallel or serially.
  • A control script executes instances of the simulation, varying parameters as necessary.
  • Data is collected and stored for plotting and analysis using external scripts and existing tools.
  • Measures within the ns-3 core are taken by connecting the stat framework to existing trace signals.
  • Trace signals or direct manipulation of the framework may be used to instrument custom simulation code.

Those basic components of the framework and their interactions are depicted in the following figure.

_images/Stat-framework-arch.png

Example

This section goes through the process of constructing an experiment in the framework and producing data for analysis (graphs) from it, demonstrating the structure and API along the way.

Question

‘’What is the (simulated) performance of ns-3’s WiFi NetDevices (using the default settings)? How far apart can wireless nodes be in a simulation before they cannot communicate reliably?’‘

  • Hypothesis: Based on knowledge of real life performance, the nodes should communicate reasonably well to at least 100m apart. Communication beyond 200m shouldn’t be feasible.

Although not a very common question in simulation contexts, this is an important property of which simulation developers should have a basic understanding. It is also a common study done on live hardware.

Simulation Program

The first thing to do in implementing this experiment is developing the simulation program. The code for this example can be found in examples/stats/wifi-example-sim.cc. It does the following main steps.

  • Declaring parameters and parsing the command line using ns3::CommandLine.

    double distance = 50.0;
    string format ("OMNet++");
    string experiment ("wifi-distance-test");
    string strategy ("wifi-default");
    string runID;
    
    CommandLine cmd;
    cmd.AddValue("distance",   "Distance apart to place nodes (in meters).", distance);
    cmd.AddValue("format",     "Format to use for data output.",             format);
    cmd.AddValue("experiment", "Identifier for experiment.",                 experiment);
    cmd.AddValue("strategy",   "Identifier for strategy.",                   strategy);
    cmd.AddValue("run",        "Identifier for run.",                        runID);
    cmd.Parse (argc, argv);
    
  • Creating nodes and network stacks using ns3::NodeContainer, ns3::WiFiHelper, and ns3::InternetStackHelper.

    NodeContainer nodes;
    nodes.Create(2);
    
    WifiHelper wifi;
    wifi.SetMac("ns3::AdhocWifiMac");
    wifi.SetPhy("ns3::WifiPhy");
    NetDeviceContainer nodeDevices = wifi.Install(nodes);
    
    InternetStackHelper internet;
    internet.Install(nodes);
    Ipv4AddressHelper ipAddrs;
    ipAddrs.SetBase("192.168.0.0", "255.255.255.0");
    ipAddrs.Assign(nodeDevices);
    
  • Positioning the nodes using ns3::MobilityHelper. By default the nodes have static mobility and won’t move, but must be positioned the given distance apart. There are several ways to do this; it is done here using ns3::ListPositionAllocator, which draws positions from a given list.

    MobilityHelper mobility;
    Ptr<ListPositionAllocator> positionAlloc =
      CreateObject<ListPositionAllocator>();
    positionAlloc->Add(Vector(0.0, 0.0, 0.0));
    positionAlloc->Add(Vector(0.0, distance, 0.0));
    mobility.SetPositionAllocator(positionAlloc);
    mobility.Install(nodes);
    
  • Installing a traffic generator and a traffic sink. The stock Applications could be used, but the example includes custom objects in src/test/test02-apps.(cc|h). These have a simple behavior, generating a given number of packets spaced at a given interval. As there is only one of each they are installed manually; for a larger set the ns3::ApplicationHelper class could be used. The commented-out Config::Set line changes the destination of the packets, set to broadcast by default in this example. Note that in general WiFi may have different performance for broadcast and unicast frames due to different rate control and MAC retransmission policies.

    Ptr<Node> appSource = NodeList::GetNode(0);
    Ptr<Sender> sender = CreateObject<Sender>();
    appSource->AddApplication(sender);
    sender->Start(Seconds(1));
    
    Ptr<Node> appSink = NodeList::GetNode(1);
    Ptr<Receiver> receiver = CreateObject<Receiver>();
    appSink->AddApplication(receiver);
    receiver->Start(Seconds(0));
    
    //  Config::Set("/NodeList/*/ApplicationList/*/$Sender/Destination",
    //              Ipv4AddressValue("192.168.0.2"));
    
  • Configuring the data and statistics to be collected. The basic paradigm is that an ns3::DataCollector object is created to hold information about this particular run, to which observers and calculators are attached to actually generate data. Importantly, run information includes labels for the ‘’experiment’‘, ‘’strategy’‘, ‘’input’‘, and ‘’run’‘. These are used to later identify and easily group data from multiple trials.

    • The experiment is the study of which this trial is a member. Here it is on WiFi performance and distance.
    • The strategy is the code or parameters being examined in this trial. In this example it is fixed, but an obvious extension would be to investigate different WiFi bit rates, each of which would be a different strategy.
    • The input is the particular problem given to this trial. Here it is simply the distance between the two nodes.
    • The runID is a unique identifier for this trial with which it’s information is tagged for identification in later analysis. If no run ID is given the example program makes a (weak) run ID using the current time.

    Those four pieces of metadata are required, but more may be desired. They may be added to the record using the ns3::DataCollector::AddMetadata() method.

    DataCollector data;
    data.DescribeRun(experiment, strategy, input, runID);
    data.AddMetadata("author", "tjkopena");
    

    Actual observation and calculating is done by ns3::DataCalculator objects, of which several different types exist. These are created by the simulation program, attached to reporting or sampling code, and then registered with the ns3::DataCollector so they will be queried later for their output. One easy observation mechanism is to use existing trace sources, for example to instrument objects in the ns-3 core without changing their code. Here a counter is attached directly to a trace signal in the WiFi MAC layer on the target node.

    Ptr<PacketCounterCalculator> totalRx = CreateObject<PacketCounterCalculator>();
    totalRx->SetKey("wifi-rx-frames");
    Config::Connect("/NodeList/1/DeviceList/*/$ns3::WifiNetDevice/Rx",
                    MakeCallback(&PacketCounterCalculator::FrameUpdate, totalRx));
    data.AddDataCalculator(totalRx);
    

    Calculators may also be manipulated directly. In this example, a counter is created and passed to the traffic sink application to be updated when packets are received.

    Ptr<CounterCalculator<> > appRx = CreateObject<CounterCalculator<> >();
    appRx->SetKey("receiver-rx-packets");
    receiver->SetCounter(appRx);
    data.AddDataCalculator(appRx);
    

    To increment the count, the sink’s packet processing code then calls one of the calculator’s update methods.

    m_calc->Update();
    

    The program includes several other examples as well, using both the primitive calculators such as ns3::CounterCalculator and those adapted for observing packets and times. In src/test/test02-apps.(cc|h) it also creates a simple custom tag which it uses to track end-to-end delay for generated packets, reporting results to a ns3::TimeMinMaxAvgTotalCalculator data calculator.

  • Running the simulation, which is very straightforward once constructed.

    Simulator::Run();
    
  • Generating either OMNet++ or SQLite output, depending on the command line arguments. To do this a ns3::DataOutputInterface object is created and configured. The specific type of this will determine the output format. This object is then given the ns3::DataCollector object which it interrogates to produce the output.

    Ptr<DataOutputInterface> output;
    if (format == "OMNet++") {
      NS_LOG_INFO("Creating OMNet++ formatted data output.");
      output = CreateObject<OmnetDataOutput>();
    } else {
    #   ifdef STAT_USE_DB
        NS_LOG_INFO("Creating SQLite formatted data output.");
        output = CreateObject<SqliteDataOutput>();
    #   endif
    }
    
    output->Output(data);
    
  • Freeing any memory used by the simulation. This should come at the end of the main function for the example.

    Simulator::Destroy();
    
Logging

To see what the example program, applications, and stat framework are doing in detail, set the NS_LOG variable appropriately. The following will provide copious output from all three.

$ export NS_LOG=WiFiDistanceExperiment:WiFiDistanceApps

Note that this slows down the simulation extraordinarily.

Sample Output

Compiling and simply running the test program will append OMNet++ formatted output such as the following to data.sca.

run run-1212239121

attr experiment "wifi-distance-test"
attr strategy "wifi-default"
attr input "50"
attr description ""

attr "author" "tjkopena"

scalar wifi-tx-frames count 30
scalar wifi-rx-frames count 30
scalar sender-tx-packets count 30
scalar receiver-rx-packets count 30
scalar tx-pkt-size count 30
scalar tx-pkt-size total 1920
scalar tx-pkt-size average 64
scalar tx-pkt-size max 64
scalar tx-pkt-size min 64
scalar delay count 30
scalar delay total 5884980ns
scalar delay average 196166ns
scalar delay max 196166ns
scalar delay min 196166ns
Control Script

In order to automate data collection at a variety of inputs (distances), a simple Bash script is used to execute a series of simulations. It can be found at examples/stats/wifi-example-db.sh. The script is meant to be run from the examples/stats/ directory.

The script runs through a set of distances, collecting the results into an SQLite database. At each distance five trials are conducted to give a better picture of expected performance. The entire experiment takes only a few dozen seconds to run on a low end machine as there is no output during the simulation and little traffic is generated.

#!/bin/sh

DISTANCES="25 50 75 100 125 145 147 150 152 155 157 160 162 165 167 170 172 175 177 180"
TRIALS="1 2 3 4 5"

echo WiFi Experiment Example

if [ -e data.db ]
then
  echo Kill data.db?
  read ANS
  if [ "$ANS" = "yes" -o "$ANS" = "y" ]
  then
    echo Deleting database
    rm data.db
  fi
fi

for trial in $TRIALS
do
  for distance in $DISTANCES
  do
    echo Trial $trial, distance $distance
    ./bin/test02 --format=db --distance=$distance --run=run-$distance-$trial
  done
done
Analysis and Conclusion

Once all trials have been conducted, the script executes a simple SQL query over the database using the SQLite command line program. The query computes average packet loss in each set of trials associated with each distance. It does not take into account different strategies, but the information is present in the database to make some simple extensions and do so. The collected data is then passed to GNUPlot for graphing.

CMD="select exp.input,avg(100-((rx.value*100)/tx.value)) \
    from Singletons rx, Singletons tx, Experiments exp \
    where rx.run = tx.run AND \
          rx.run = exp.run AND \
          rx.name='receiver-rx-packets' AND \
          tx.name='sender-tx-packets' \
    group by exp.input \
    order by abs(exp.input) ASC;"

sqlite3 -noheader data.db "$CMD" > wifi-default.data
sed -i "s/|/   /" wifi-default.data
gnuplot wifi-example.gnuplot

The GNUPlot script found at examples/stats/wifi-example.gnuplot simply defines the output format and some basic formatting for the graph.

set terminal postscript portrait enhanced lw 2 "Helvetica" 14

set size 1.0, 0.66

#-------------------------------------------------------
set out "wifi-default.eps"
#set title "Packet Loss Over Distance"
set xlabel "Distance (m) --- average of 5 trials per point"
set xrange [0:200]
set ylabel "% Packet Loss"
set yrange [0:110]

plot "wifi-default.data" with lines title "WiFi Defaults"
End Result

The resulting graph provides no evidence that the default WiFi model’s performance is necessarily unreasonable and lends some confidence to an at least token faithfulness to reality. More importantly, this simple investigation has been carried all the way through using the statistical framework. Success!

_images/Wifi-default.png

Scope/Limitations

This section discusses the scope and limitations of the Data Collection Framework.

Currently, only these Probes have been implemented in DCF:

  • BooleanProbe
  • DoubleProbe
  • Uinteger8Probe
  • Uinteger16Probe
  • Uinteger32Probe
  • PacketProbe
  • ApplicationPacketProbe
  • Ipv4PacketProbe

Currently, no Collectors are available in the DCF, although a BasicStatsCollector is under development.

Currently, only these Aggregators have been implemented in DCF:

  • GnuplotAggregator
  • FileAggregator

Currently, only this Adaptor has been implemented in DCF:

Time-Series Adaptor.

Future Work

This section discusses the future work to be done on the Data Collection Framework.

Here are some things that still need to be done:

  • Hook up more trace sources in ns-3 code to get more values out of the simulator.
  • Implement more types of Probes than there currently are.
  • Implement more than just the single current 2-D Collector, BasicStatsCollector.
  • Implement more Aggregators.
  • Implement more than just Adaptors.

DSDV Routing

Destination-Sequenced Distance Vector (DSDV) routing protocol is a pro-active, table-driven routing protocol for MANETs developed by Charles E. Perkins and Pravin Bhagwat in 1994. It uses the hop count as metric in route selection.

This model was developed by the ResiliNets research group at the University of Kansas. A paper on this model exists at this URL.

DSDV Routing Overview

DSDV Routing Table: Every node will maintain a table listing all the other nodes it has known either directly or through some neighbors. Every node has a single entry in the routing table. The entry will have information about the node’s IP address, last known sequence number and the hop count to reach that node. Along with these details the table also keeps track of the nexthop neighbor to reach the destination node, the timestamp of the last update received for that node.

The DSDV update message consists of three fields, Destination Address, Sequence Number and Hop Count.

Each node uses 2 mechanisms to send out the DSDV updates. They are,

  1. Periodic Updates

    Periodic updates are sent out after every m_periodicUpdateInterval(default:15s). In this update the node broadcasts out its entire routing table.

  2. Trigger Updates

    Trigger Updates are small updates in-between the periodic updates. These updates are sent out whenever a node receives a DSDV packet that caused a change in its routing table. The original paper did not clearly mention when for what change in the table should a DSDV update be sent out. The current implemntation sends out an update irrespective of the change in the routing table.

The updates are accepted based on the metric for a particular node. The first factor determinig the acceptance of an update is the sequence number. It has to accept the update if the sequence number of the update message is higher irrespective of the metric. If the update with same sequence number is received, then the update with least metric (hopCount) is given precedence.

In highly mobile scenarios, there is a high chance of route fluctuations, thus we have the concept of weighted settling time where an update with change in metric will not be advertised to neighbors. The node waits for the settling time to make sure that it did not receive the update from its old neighbor before sending out that update.

The current implementation covers all the above features of DSDV. The current implementation also has a request queue to buffer packets that have no routes to destination. The default is set to buffer up to 5 packets per destination.

DSR Routing

Dynamic Source Routing (DSR) protocol is a reactive routing protocol designed specifically for use in multi-hop wireless ad hoc networks of mobile nodes.

This model was developed by the ResiliNets research group at the University of Kansas.

DSR Routing Overview

This model implements the base specification of the Dynamic Source Routing (DSR) protocol. Implementation is based on RFC 4728, with some extensions and modifications to the RFC specifications.

DSR operates on a on-demand behavior. Therefore, our DSR model buffers all packets while a route request packet (RREQ) is disseminated. We implement a packet buffer in dsr-rsendbuff.cc. The packet queue implements garbage collection of old packets and a queue size limit. When the packet is sent out from the send buffer, it will be queued in maintenance buffer for next hop acknowledgment.

The maintenance buffer then buffers the already sent out packets and waits for the notification of packet delivery. Protocol operation strongly depends on broken link detection mechanism. We implement the three heuristics recommended based the RFC as follows:

First, we use link layer feedback when possible, which is also the fastest mechanism of these three to detect link errors. A link is considered to be broken if frame transmission results in a transmission failure for all retries. This mechanism is meant for active links and works much faster than in its absence. DSR is able to detect the link layer transmission failure and notify that as broken. Recalculation of routes will be triggered when needed. If user does not want to use link layer acknowledgment, it can be tuned by setting “LinkAcknowledgment” attribute to false in “dsr-routing.cc”.

Second, passive acknowledgment should be used whenever possible. The node turns on “promiscuous” receive mode, in which it can receive packets not destined for itself, and when the node assures the delivery of that data packet to its destination, it cancels the passive acknowledgment timer.

Last, we use a network layer acknowledge scheme to notify the receipt of a packet. Route request packet will not be acknowledged or retransmitted.

The Route Cache implementation support garbage collection of old entries and state machine, as defined in the standard. It implements as a STL map container. The key is the destination IP address.

DSR operates with direct access to IP header, and operates between network and transport layer. When packet is sent out from transport layer, it passes itself to DSR and DSR header is appended.

We have two caching mechanisms: path cache and link cache. The path cache saves the whole path in the cache. The paths are sorted based on the hop count, and whenever one path is not able to be used, we change to the next path. The link cache is a slightly better design in the sense that it uses different subpaths and uses Implemented Link Cache using Dijsktra algorithm, and this part is implemented by Song Luan <lsuper@mail.ustc.edu.cn>.

The following optional protocol optimizations aren’t implemented:

  • Flow state

  • First Hop External (F), Last Hop External (L) flags

  • Handling unknown DSR options

  • Two types of error headers:
    1. flow state not supported option
    2. unsupported option (not going to happen in simulation)

DSR update in ns-3.17

We originally used “TxErrHeader” in Ptr<WifiMac> to indicate the transmission error of a specific packet in link layer, however, it was not working quite correctly since even when the packet was dropped, this header was not recorded in the trace file. We used to a different path on implementing the link layer notification mechanism. We look into the trace file by finding packet receive event. If we find one receive event for the data packet, we count that as the indicator for successful data delivery.

Useful parameters

+------------------------- +------------------------------------+-------------+
| Parameter                | Description                        | Default     |
+==========================+====================================+=============+
| MaxSendBuffLen           | Maximum number of packets that can | 64          |
|                          | be stored in send buffer           |             |
+------------------------- +------------------------------------+-------------+
| MaxSendBuffTime          | Maximum time packets can be queued | Seconds(30) |
|                          | in the send buffer                 |             |
+------------------------- +------------------------------------+-------------+
| MaxMaintLen              | Maximum number of packets that can | 50          |
|                          | be stored in maintenance buffer    |             |
+------------------------- +------------------------------------+-------------+
| MaxMaintTime             | Maximum time packets can be queued | Seconds(30) |
|                          | in maintenance buffer              |             |
+------------------------- +------------------------------------+-------------+
| MaxCacheLen              | Maximum number of route entries    | 64          |
|                          | that can be stored in route cache  |             |
+------------------------- +------------------------------------+-------------+
| RouteCacheTimeout        | Maximum time the route cache can   | Seconds(300)|
|                          | be queued in route cache           |             |
+------------------------- +------------------------------------+-------------+
| RreqRetries              | Maximum number of retransmissions  | 16          |
|                          | for request discovery of a route   |             |
+------------------------- +------------------------------------+-------------+
| CacheType                | Use Link Cache or use Path Cache   | "LinkCache" |
|                          |                                    |             |
+------------------------- +------------------------------------+-------------+
| LinkAcknowledgment       | Enable Link layer acknowledgment   | True        |
|                          | mechanism                          |             |
+------------------------- +------------------------------------+-------------+

Implementation modification

  • The DsrFsHeader has added 3 fields: message type, source id, destination id, and these changes only for post-processing
    1. Message type is used to identify the data packet from control packet
    2. source id is used to identify the real source of the data packet since we have to deliver the packet hop-by-hop and the ipv4header is not carrying the real source and destination ip address as needed
    3. destination id is for same reason of above
  • Route Reply header is not word-aligned in DSR RFC, change it to word-aligned in implementation

  • DSR works as a shim header between transport and network protocol, it needs its own forwarding mechanism, we are changing the packet transmission to hop-by-hop delivery, so we added two fields in dsr fixed header to notify packet delivery

Current Route Cache implementation

This implementation used “path cache”, which is simple to implement and ensures loop-free paths:

  • the path cache has automatic expire policy
  • the cache saves multiple route entries for a certain destination and sort the entries based on hop counts
  • the MaxEntriesEachDst can be tuned to change the maximum entries saved for a single destination
  • when adding mulitiple routes for one destination, the route is compared based on hop-count and expire time, the one with less hop count or relatively new route is favored
  • Future implementation may include “link cache” as another possibility

DSR Instructions

The following should be kept in mind when running DSR as routing protocol:

  • NodeTraversalTime is the time it takes to traverse two neighboring nodes and should be chosen to fit the transmission range
  • PassiveAckTimeout is the time a packet in maintenance buffer wait for passive acknowledgment, normally set as two times of NodeTraversalTime
  • RouteCacheTimeout should be set smaller value when the nodes’ velocity become higher. The default value is 300s.

Helper

To have a node run DSR, the easiest way would be to use the DsrHelper and DsrMainHelpers in your simulation script. For instance:

DsrHelper dsr;
DsrMainHelper dsrMain;
dsrMain.Install (dsr, adhocNodes);

The example scripts inside src/dsr/examples/ demonstrate the use of DSR based nodesin different scenarios. The helper source can be found inside src/dsr/helper/dsr-main-helper.{h,cc} and src/dsr/helper/dsr-helper.{h,cc}

Examples

The example can be found in src/dsr/examples/:

  • dsr.cc use DSR as routing protocol within a traditional MANETs environment[3].

DSR is also built in the routing comparison case in examples/routing/:

  • manet-routing-compare.cc is a comparison case with built in MANET routing protocols and can generate its own results.

Validation

This model has been tested as follows:

  • Unit tests have been written to verify the internals of DSR. This can be found in src/dsr/test/dsr-test-suite.cc. These tests verify whether the methods inside DSR module which deal with packet buffer, headers work correctly.
  • Simulation cases similar to [3] have been tested and have comparable results.
  • manet-routing-compare.cc has been used to compare DSR with three of other routing protocols.

A paper was presented on these results at the Workshop on ns-3 in 2011.

Limitations

The model is not fully compliant with RFC 4728. As an example, Dsr fixed size header has been extended and it is four octects longer then the RFC specification. As a consequence, the DSR headers can not be correctly decoded by Wireshark.

The model full compliance with the RFC is planned for the future.

Emulation Overview

ns-3 has been designed for integration into testbed and virtual machine environments. We have addressed this need by providing two kinds of net devices. The first kind of device is a file descriptor net device (FdNetDevice), which is a generic device type that can read and write from a file descriptor. By associating this file descriptor with different things on the host system, different capabilities can be provided. For instance, the FdNetDevice can be associated with an underlying packet socket to provide emulation capabilities. This allows ns-3 simulations to send data on a “real” network. The second kind, called a Tap NetDevice allows a “real” host to participate in an ns-3 simulation as if it were one of the simulated nodes. An ns-3 simulation may be constructed with any combination of simulated or emulated devices.

Note: Prior to ns-3.17, the emulation capability was provided by a special device called an Emu NetDevice; the Emu NetDevice has been superseded by the FdNetDevice, and will be deprecated and removed in future revisions of ns-3.

One of the use-cases we want to support is that of a testbed. A concrete example of an environment of this kind is the ORBIT testbed. ORBIT is a laboratory emulator/field trial network arranged as a two dimensional grid of 400 802.11 radio nodes. We integrate with ORBIT by using their “imaging” process to load and run ns-3 simulations on the ORBIT array. We can use our EmuFdNetDevice to drive the hardware in the testbed and we can accumulate results either using the ns-3 tracing and logging functions, or the native ORBIT data gathering techniques. See http://www.orbit-lab.org/ for details on the ORBIT testbed.

A simulation of this kind is shown in the following figure:

_images/testbed.png

Example Implementation of Testbed Emulation.

You can see that there are separate hosts, each running a subset of a “global” simulation. Instead of an ns-3 channel connecting the hosts, we use real hardware provided by the testbed. This allows ns-3 applications and protocol stacks attached to a simulation node to communicate over real hardware.

We expect the primary use for this configuration will be to generate repeatable experimental results in a real-world network environment that includes all of the ns-3 tracing, logging, visualization and statistics gathering tools.

In what can be viewed as essentially an inverse configuration, we allow “real” machines running native applications and protocol stacks to integrate with an ns-3 simulation. This allows for the simulation of large networks connected to a real machine, and also enables virtualization. A simulation of this kind is shown in the following figure:

_images/emulated-channel.png

Implementation overview of emulated channel.

Here, you will see that there is a single host with a number of virtual machines running on it. An ns-3 simulation is shown running in the virtual machine shown in the center of the figure. This simulation has a number of nodes with associated ns-3 applications and protocol stacks that are talking to an ns-3 channel through native simulated ns-3 net devices.

There are also two virtual machines shown at the far left and far right of the figure. These VMs are running native (Linux) applications and protocol stacks. The VM is connected into the simulation by a Linux Tap net device. The user-mode handler for the Tap device is instantiated in the simulation and attached to a proxy node that represents the native VM in the simulation. These handlers allow the Tap devices on the native VMs to behave as if they were ns-3 net devices in the simulation VM. This, in turn, allows the native software and protocol suites in the native VMs to believe that they are connected to the simulated ns-3 channel.

We expect the typical use case for this environment will be to analyze the behavior of native applications and protocol suites in the presence of large simulated ns-3 networks.

Emu NetDevice

Note: The EmuNetDevice is being deprecated as of ns-3.17. The File Descriptor NetDevice and EmuFdNetDeviceHelper class replaces this functionality and API entirely.

Behavior

The Emu net device allows a simulation node to send and receive packets over a real network. The emulated net device relies on a specified interface being in promiscuous mode. It opens a raw socket and binds to that interface. We perform MAC spoofing to separate simulation network traffic from other network traffic that may be flowing to and from the host.

One can use the Emu net device in a testbed situation where the host on which the simulation is running has a specific interface of interest which drives the testbed hardware. You would also need to set this specific interface into promiscuous mode and provide an appropriate device name to the ns-3 emulated net device. An example of this environment is the ORBIT testbed as described above.

The Emu net device only works if the underlying interface is up and in promiscuous mode. Packets will be sent out over the device, but we use MAC spoofing. The MAC addresses will be generated (by default) using the Organizationally Unique Identifier (OUI) 00:00:00 as a base. This vendor code is not assigned to any organization and so should not conflict with any real hardware.

It is always up to the user to determine that using these MAC addresses is okay on your network and won’t conflict with anything else (including another simulation using Emu devices) on your network. If you are using the emulated net device in separate simulations you must consider global MAC address assignment issues and ensure that MAC addresses are unique across all simulations. The emulated net device respects the MAC address provided in the SetAddress method so you can do this manually. For larger simulations, you may want to set the OUI in the MAC address allocation function.

IP addresses corresponding to the emulated net devices are the addresses generated in the simulation, which are generated in the usual way via helper functions. Since we are using MAC spoofing, there will not be a conflict between ns-3 network stacks and any native network stacks.

The emulated net device comes with a helper function as all ns-3 devices do. One unique aspect is that there is no channel associated with the underlying medium. We really have no idea what this external medium is, and so have not made an effort to model it abstractly. The primary thing to be aware of is the implication this has for IPv4 global routing. The global router module attempts to walk the channels looking for adjacent networks. Since there is no channel, the global router will be unable to do this and you must then use a dynamic routing protocol such as OLSR to include routing in Emu-based networks.

Usage

Any mixing of ns-3 objects with real objects will typically require that ns-3 compute checksums in its protocols. By default, checksums are not computed by ns-3. To enable checksums (e.g. UDP, TCP, IP), users must set the attribute ChecksumEnabled to true, such as follows:

GlobalValue::Bind ("ChecksumEnabled", BooleanValue (true));

The usage of the Emu net device is straightforward once the network of simulations has been configured. Since most of the work involved in working with this device is in network configuration before even starting a simulation, you may want to take a moment to review a couple of HOWTO pages on the ns-3 wiki that describe how to set up a virtual test network using VMware and how to run a set of example (client server) simulations that use Emu net devices.

Once you are over the configuration hurdle, the script changes required to use an Emu device are trivial. The main structural difference is that you will need to create an ns-3 simulation script for each node. In the case of the HOWTOs above, there is one client script and one server script. The only “challenge” is to get the addresses set correctly.

Just as with all other ns-3 net devices, we provide a helper class for the Emu net device. The following code snippet illustrates how one would declare an EmuHelper and use it to set the “DeviceName” attribute to “eth1” and install Emu devices on a group of nodes. You would do this on both the client and server side in the case of the HOWTO seen above.:

EmuHelper emu;
emu.SetAttribute ("DeviceName", StringValue ("eth1"));
NetDeviceContainer d = emu.Install (n);

The only other change that may be required is to make sure that the address spaces (MAC and IP) on the client and server simulations are compatible. First the MAC address is set to a unique well-known value in both places (illustrated here for one side).:

//
// We've got the devices in place.  Since we're using MAC address
// spoofing under the sheets, we need to make sure that the MAC addresses
// we have assigned to our devices are unique.  Ns-3 will happily
// automatically assign the same MAC address to the devices in both halves
// of our two-script pair, so let's go ahead and just manually change them
// to something we ensure is unique.
//
Ptr<NetDevice> nd = d.Get (0);
Ptr<EmuNetDevice> ed = nd->GetObject<EmuNetDevice> ();
ed->SetAddress ("00:00:00:00:00:02");

And then the IP address of the client or server is set in the usual way using helpers.:

//
// We've got the "hardware" in place.  Now we need to add IP addresses.
// This is the server half of a two-script pair.  We need to make sure
// that the addressing in both of these applications is consistent, so
// we use provide an initial address in both cases.  Here, the client
// will reside on one machine running ns-3 with one node having ns-3
// with IP address "10.1.1.2" and talk to a server script running in
// another ns-3 on another computer that has an ns-3 node with IP
// address "10.1.1.3"
//
Ipv4AddressHelper ipv4;
ipv4.SetBase ("10.1.1.0", "255.255.255.0", "0.0.0.2");
Ipv4InterfaceContainer i = ipv4.Assign (d);

You will use application helpers to generate traffic exactly as you do in any ns-3 simulation script. Note that the server address shown below in a snippet from the client, must correspond to the IP address assigned to the server node similarly to the snippet above.:

uint32_t packetSize = 1024;
uint32_t maxPacketCount = 2000;
Time interPacketInterval = Seconds (0.001);
UdpEchoClientHelper client ("10.1.1.3", 9);
client.SetAttribute ("MaxPackets", UintegerValue (maxPacketCount));
client.SetAttribute ("Interval", TimeValue (interPacketInterval));
client.SetAttribute ("PacketSize", UintegerValue (packetSize));
ApplicationContainer apps = client.Install (n.Get (0));
apps.Start (Seconds (1.0));
apps.Stop (Seconds (2.0));

The Emu net device and helper provide access to ASCII and pcap tracing functionality just as other ns-3 net devices to. You enable tracing similarly to these other net devices:

EmuHelper::EnablePcapAll ("emu-udp-echo-client");

For examples that use the Emu net device, see src/emu/examples/emu-udp-echo.cc and src/emu/examples/emu-ping.cc in the repository.

Implementation

Perhaps the most unusual part of the Emu and Tap device implementation relates to the requirement for executing some of the code with super-user permissions. Rather than force the user to execute the entire simulation as root, we provide a small “creator” program that runs as root and does any required high-permission sockets work.

We do a similar thing for both the Emu and the Tap devices. The high-level view is that the CreateSocket method creates a local interprocess (Unix) socket, forks, and executes the small creation program. The small program, which runs as suid root, creates a raw socket and sends back the raw socket file descriptor over the Unix socket that is passed to it as a parameter. The raw socket is passed as a control message (sometimes called ancillary data) of type SCM_RIGHTS.

The Emu net device uses the ns-3 threading and multithreaded real-time scheduler extensions. The interesting work in the Emu device is done when the net device is started (EmuNetDevice::StartDevice ()). An attribute (“Start”) provides a simulation time at which to spin up the net device. At this specified time (which defaults to t=0), the socket creation function is called and executes as described above. You may also specify a time at which to stop the device using the “Stop” attribute.

Once the (promiscuous mode) socket is created, we bind it to an interface name also provided as an attribute (“DeviceName”) that is stored internally as m_deviceName:

struct ifreq ifr;
bzero (&ifr, sizeof(ifr));
strncpy ((char *)ifr.ifr_name, m_deviceName.c_str (), IFNAMSIZ);

int32_t rc = ioctl (m_sock, SIOCGIFINDEX, &ifr);

struct sockaddr_ll ll;
bzero (&ll, sizeof(ll));

ll.sll_family = AF_PACKET;
ll.sll_ifindex = m_sll_ifindex;
ll.sll_protocol = htons(ETH_P_ALL);

rc = bind (m_sock, (struct sockaddr *)&ll, sizeof (ll));

After the promiscuous raw socket is set up, a separate thread is spawned to do reads from that socket and the link state is set to Up.:

m_readThread = Create<SystemThread> (
  MakeCallback (&EmuNetDevice::ReadThread, this));
m_readThread->Start ();

NotifyLinkUp ();

The EmuNetDevice::ReadThread function basically just sits in an infinite loop reading from the promiscuous mode raw socket and scheduling packet receptions using the real-time simulator extensions.:

for (;;)
  {
    ...

    len = recvfrom (m_sock, buf, bufferSize, 0, (struct sockaddr *)&addr,
      &addrSize);

    ...

    DynamicCast<RealtimeSimulatorImpl> (Simulator::GetImplementation ())->
      ScheduleRealtimeNow (
        MakeEvent (&EmuNetDevice::ForwardUp, this, buf, len));

    ...
  }

The line starting with our templated DynamicCast function probably deserves a comment. It gains access to the simulator implementation object using the Simulator::GetImplementation method and then casts to the real-time simulator implementation to use the real-time schedule method ScheduleRealtimeNow. This function will cause a handler for the newly received packet to be scheduled for execution at the current real time clock value. This will, in turn cause the simulation clock to be advanced to that real time value when the scheduled event (EmuNetDevice::ForwardUp) is fired.

The ForwardUp function operates as most other similar ns-3 net device methods do. The packet is first filtered based on the destination address. In the case of the Emu device, the MAC destination address will be the address of the Emu device and not the hardware address of the real device. Headers are then stripped off and the trace hooks are hit. Finally, the packet is passed up the ns-3 protocol stack using the receive callback function of the net device.

Sending a packet is equally straightforward as shown below. The first thing we do is to add the ethernet header and trailer to the ns-3 Packet we are sending. The source address corresponds to the address of the Emu device and not the underlying native device MAC address. This is where the MAC address spoofing is done. The trailer is added and we enqueue and dequeue the packet from the net device queue to hit the trace hooks.:

header.SetSource (source);
header.SetDestination (destination);
header.SetLengthType (packet->GetSize ());
packet->AddHeader (header);

EthernetTrailer trailer;
trailer.CalcFcs (packet);
packet->AddTrailer (trailer);

m_queue->Enqueue (packet);
packet = m_queue->Dequeue ();

struct sockaddr_ll ll;
bzero (&ll, sizeof (ll));

ll.sll_family = AF_PACKET;
ll.sll_ifindex = m_sll_ifindex;
ll.sll_protocol = htons(ETH_P_ALL);

rc = sendto (m_sock, packet->PeekData (), packet->GetSize (), 0,
  reinterpret_cast<struct sockaddr *> (&ll), sizeof (ll));

Finally, we simply send the packet to the raw socket which puts it out on the real network.

From the point of view of tracing in the net device, there are several interesting points to insert trace hooks. A convention inherited from other simulators is that packets destined for transmission onto attached networks pass through a single “transmit queue” in the net device. We provide trace hooks at this point in packet flow, which corresponds (abstractly) only to a transition from the network to data link layer, and call them collectively the device MAC hooks.

When a packet is sent to the Emu net device for transmission it always passes through the transmit queue. The transmit queue in the EmuNetDevice inherits from Queue, and therefore inherits three trace sources:

  • An Enqueue operation source (see Queue::m_traceEnqueue);
  • A Dequeue operation source (see Queue::m_traceDequeue);
  • A Drop operation source (see Queue::m_traceDrop).

The upper-level (MAC) trace hooks for the EmuNetDevice are, in fact, exactly these three trace sources on the single transmit queue of the device.

The m_traceEnqueue event is triggered when a packet is placed on the transmit queue. This happens at the time that ns3::EmuNetDevice::Send or ns3::EmuNetDevice::SendFrom is called by a higher layer to queue a packet for transmission.

The m_traceDequeue event is triggered when a packet is removed from the transmit queue. Dequeues from the transmit queue happen immediately after the packet was enqueued and only indicate that the packet is about to be sent to an underlying raw socket. The actual time at which the packet is sent out on the wire is not available.

Similar to the upper level trace hooks, there are trace hooks available at the lower levels of the net device. We call these the PHY hooks. These events fire from the device methods that talk directly to the underlying raw socket.

The trace source m_dropTrace is not used in the Emu net device since that is really the business of the underlying “real” device driver.

The other low-level trace source fires on reception of an accepted packet (see ns3::EmuNetDevice::m_rxTrace). A packet is accepted if it is destined for the broadcast address, a multicast address, or to the MAC address assigned to the Emu net device.

File Descriptor NetDevice

The src/fd-net-device module provides the FdNetDevice class, which is able to read and write traffic using a file descriptor provided by the user. This file descriptor can be associated to a TAP device, to a raw socket, to a user space process generating/consuming traffic, etc. The user has full freedom to define how external traffic is generated and ns-3 traffic is consumed.

Different mechanisms to associate a simulation to external traffic can be provided through helper classes. Three specific helpers are provided:

  • EmuFdNetDeviceHelper (to associate the ns-3 device with a physical device in the host machine)
  • TapFdNetDeviceHelper (to associate the ns-3 device with the file descriptor from a tap device in the host machine)
  • PlanteLabFdNetDeviceHelper (to automate the creation of tap devices in PlanetLab nodes, enabling ns-3 simulations that can send and receive traffic though the Internet using PlanetLab resource.

Model Description

The source code for this module lives in the directory src/fd-net-device.

The FdNetDevice is a special type of ns-3 NetDevice that reads traffic to and from a file descriptor. That is, unlike pure simulation NetDevice objects that write frames to and from a simulated channel, this FdNetDevice directs frames out of the simulation to a file descriptor. The file descriptor may be associated to a Linux TUN/TAP device, to a socket, or to a user-space process.

It is up to the user of this device to provide a file descriptor. The type of file descriptor being provided determines what is being modelled. For instance, if the file descriptor provides a raw socket to a WiFi card on the host machine, the device being modelled is a WiFi device.

From the conceptual “top” of the device looking down, it looks to the simulated node like a device supporting a 48-bit IEEE MAC address that can be bridged, supports broadcast, and uses IPv4 ARP or IPv6 Neighbor Discovery, although these attributes can be tuned on a per-use-case basis.

Design

The FdNetDevice implementation makes use of a reader object, extended from the FdReader class in the ns-3 src/core module, which manages a separate thread from the main ns-3 execution thread, in order to read traffic from the file descriptor.

Upon invocation of the StartDevice method, the reader object is initialized and starts the reading thread. Before device start, a file descriptor must be previously associated to the FdNetDevice with the SetFileDescriptor invocation.

The creation and configuration of the file descriptor can be left to a number of helpers, described in more detail below. When this is done, the invocation of SetFileDescriptor is responsibility of the helper and must not be directly invoked by the user.

Upon reading an incoming frame from the file descriptor, the reader will pass the frame to the ReceiveCallback method, whose task it is to schedule the reception of the frame by the device as a ns-3 simulation event. Since the new frame is passed from the reader thread to the main ns-3 simulation thread, thread-safety issues are avoided by using the ScheduleWithContext call instead of the regular Schedule call.

In order to avoid overwhelming the scheduler when the incoming data rate is too high, a counter is kept with the number of frames that are currently scheduled to be received by the device. If this counter reaches the value given by the RxQueueSize attribute in the device, then the new frame will be dropped silently.

The actual reception of the new frame by the device occurs when the scheduled FordwarUp method is invoked by the simulator. This method acts as if a new frame had arrived from a channel attached to the device. The device then decapsulates the frame, removing any layer 2 headers, and forwards it to upper network stack layers of the node. The ForwardUp method will remove the frame headers, according to the frame encapsulation type defined by the EncapsulationMode attribute, and invoke the receive callback passing an IP packet.

An extra header, the PI header, can be present when the file descriptor is associated to a TAP device that was created without setting the IFF_NO_PI flag. This extra header is removed if EncapsulationMode is set to DIXPI value.

In the opposite direction, packets generated inside the simulation that are sent out through the device, will be passed to the Send method, which will in turn invoke the SendFrom method. The latter method will add the necessary layer 2 headers, and simply write the newly created frame to the file descriptor.

Scope and Limitations

Users of this device are cautioned that there is no flow control across the file descriptor boundary, when using in emulation mode. That is, in a Linux system, if the speed of writing network packets exceeds the ability of the underlying physical device to buffer the packets, backpressure up to the writing application will be applied to avoid local packet loss. No such flow control is provided across the file descriptor interface, so users must be aware of this limitation.

As explained before, the RxQueueSize attribute limits the number of packets that can be pending to be received by the device. Frames read from the file descriptor while the number of pending packets is in its maximum will be silently dropped.

The mtu of the device defaults to the Ethernet II MTU value. However, helpers are supposed to set the mtu to the right value to reflect the characteristics of the network interface associated to the file descriptor. If no helper is used, then the responsibility of setting the correct mtu value for the device falls back to the user. The size of the read buffer on the file descriptor reader is set to the mtu value in the StartDevice method.

The FdNetDevice class currently supports three encapsulation modes, DIX for Ethernet II frames, LLC for 802.2 LLC/SNAP frames, and DIXPI for Ethernet II frames with an additional TAP PI header. This means that traffic traversing the file descriptor is expected to be Ethernet II compatible. Attaching an FdNetDevice to a wireless interface is possible as long as the driver provides Ethernet II frames to the socket API. Note that to associate a FdNetDevice to a wireless card in ad-hoc mode, the MAC address of the device must be set to the real card MAC address, else any incoming traffic a fake MAC address will be discarded by the driver.

As mentioned before, three helpers are provided with the fd-net-device module. Each individual helper (file descriptor type) may have platform limitations. For instance, threading, real-time simulation mode, and the ability to create TUN/TAP devices are prerequisites to using the provided helpers. Support for these modes can be found in the output of the waf configure step, e.g.:

Threading Primitives          : enabled
Real Time Simulator           : enabled
Emulated Net Device           : enabled
Tap Bridge                    : enabled

It is important to mention that while testing the FdNetDevice we have found an upper bound limit for TCP throughput when using 1Gb Ethernet links of 60Mbps. This limit is most likely due to the processing power of the computers involved in the tests.

Usage

The usage pattern for this type of device is similar to other net devices with helpers that install to node pointers or node containers. When using the base FdNetDeviceHelper the user is responsible for creating and setting the file descriptor by himself.

FdNetDeviceHelper fd;
NetDeviceContainer devices = fd.Install (nodes);

// file descriptor generation
...

device->SetFileDescriptor (fd);

Most commonly a FdNetDevice will be used to interact with the host system. In these cases it is almost certain that the user will want to run in real-time emulation mode, and to enable checksum computations. The typical program statements are as follows:

GlobalValue::Bind ("SimulatorImplementationType", StringValue ("ns3::RealtimeSimulatorImpl"));
GlobalValue::Bind ("ChecksumEnabled", BooleanValue (true));

The easiest way to set up an experiment that interacts with a Linux host system is to user the Emu and Tap helpers. Perhaps the most unusual part of these helper implementations relates to the requirement for executing some of the code with super-user permissions. Rather than force the user to execute the entire simulation as root, we provide a small “creator” program that runs as root and does any required high-permission sockets work. The easiest way to set the right privileges for the “creator” programs, is by enabling the --enable-sudo flag when performing waf configure.

We do a similar thing for both the Emu and the Tap devices. The high-level view is that the CreateFileDescriptor method creates a local interprocess (Unix) socket, forks, and executes the small creation program. The small program, which runs as suid root, creates a raw socket and sends back the raw socket file descriptor over the Unix socket that is passed to it as a parameter. The raw socket is passed as a control message (sometimes called ancillary data) of type SCM_RIGHTS.

Helpers
EmuFdNetDeviceHelper

The EmuFdNetDeviceHelper creates a raw socket to an underlying physical device, and provides the socket descriptor to the FdNetDevice. This allows the ns-3 simulation to read frames from and write frames to a network device on the host.

The emulation helper permits to transparently integrate a simulated ns-3 node into a network composed of real nodes.

+----------------------+     +-----------------------+
|         host 1       |     |         host 2        |
+----------------------+     +-----------------------+
|    ns-3 simulation   |     |                       |
+----------------------+     |         Linux         |
|       ns-3 Node      |     |     Network Stack     |
|  +----------------+  |     |   +----------------+  |
|  |    ns-3 TCP    |  |     |   |       TCP      |  |
|  +----------------+  |     |   +----------------+  |
|  |    ns-3 IP     |  |     |   |       IP       |  |
|  +----------------+  |     |   +----------------+  |
|  |   FdNetDevice  |  |     |   |                |  |
|  |    10.1.1.1    |  |     |   |                |  |
|  +----------------+  |     |   +    ETHERNET    +  |
|  |   raw socket   |  |     |   |                |  |
|--+----------------+--|     |   +----------------+  |
|       | eth0 |       |     |        | eth0 |       |
+-------+------+-------+     +--------+------+-------+

        10.1.1.11                     10.1.1.12

            |                            |
            +----------------------------+

This helper replaces the functionality of the EmuNetDevice found in ns-3 prior to ns-3.17, by bringing this type of device into the common framework of the FdNetDevice. The EmuNetDevice will be deprecated in favor of this new helper.

The device is configured to perform MAC spoofing to separate simulation network traffic from other network traffic that may be flowing to and from the host.

One can use this helper in a testbed situation where the host on which the simulation is running has a specific interface of interest which drives the testbed hardware. You would also need to set this specific interface into promiscuous mode and provide an appropriate device name to the ns-3 simulation.

The helper only works if the underlying interface is up and in promiscuous mode. Packets will be sent out over the device, but we use MAC spoofing. The MAC addresses will be generated (by default) using the Organizationally Unique Identifier (OUI) 00:00:00 as a base. This vendor code is not assigned to any organization and so should not conflict with any real hardware.

It is always up to the user to determine that using these MAC addresses is okay on your network and won’t conflict with anything else (including another simulation using such devices) on your network. If you are using the emulated FdNetDevice configuration in separate simulations, you must consider global MAC address assignment issues and ensure that MAC addresses are unique across all simulations. The emulated net device respects the MAC address provided in the Address attribute so you can do this manually. For larger simulations, you may want to set the OUI in the MAC address allocation function.

Before invoking the Install method, the correct device name must be configured on the helper using the SetDeviceName method. The device name is required to identify which physical device should be used to open the raw socket.

EmuFdNetDeviceHelper emu;
emu.SetDeviceName (deviceName);
NetDeviceContainer devices = emu.Install (node);
Ptr<NetDevice> device = devices.Get (0);
device->SetAttribute ("Address", Mac48AddressValue (Mac48Address::Allocate ()));
TapFdNetDeviceHelper

A Tap device is a special type of Linux device for which one end of the device appears to the kernel as a virtual net_device, and the other end is provided as a file descriptor to user-space. This file descriptor can be passed to the FdNetDevice. Packets forwarded to the TAP device by the kernel will show up in the FdNetDevice in ns-3.

Users should note that this usage of TAP devices is different than that provided by the TapBridge NetDevice found in src/tap-bridge. The model in this helper is as follows:

+-------------------------------------+
|                host                 |
+-------------------------------------+
|    ns-3 simulation   |              |
+----------------------+              |
|      ns-3 Node       |              |
|  +----------------+  |              |
|  |    ns-3 TCP    |  |              |
|  +----------------+  |              |
|  |    ns-3 IP     |  |              |
|  +----------------+  |              |
|  |   FdNetDevice  |  |              |
|--+----------------+--+    +------+  |
|       | TAP  |            | eth0 |  |
|       +------+            +------+  |
|     192.168.0.1               |     |
+-------------------------------|-----+
                                |
                                |
                                ------------ (Internet) -----

In the above, the configuration requires that the host be able to forward traffic generated by the simulation to the Internet.

The model in TapBridge (in another module) is as follows:

+--------+
|  Linux |
|  host  |                    +----------+
| ------ |                    |   ghost  |
|  apps  |                    |   node   |
| ------ |                    | -------- |
|  stack |                    |    IP    |     +----------+
| ------ |                    |   stack  |     |   node   |
|  TAP   |                    |==========|     | -------- |
| device | <----- IPC ------> |   tap    |     |    IP    |
+--------+                    |  bridge  |     |   stack  |
                              | -------- |     | -------- |
                              |   ns-3   |     |   ns-3   |
                              |   net    |     |   net    |
                              |  device  |     |  device  |
                              +----------+     +----------+
                                   ||               ||
                              +---------------------------+
                              |        ns-3 channel       |
                              +---------------------------+

In the above, packets instead traverse ns-3 NetDevices and Channels.

The usage pattern for this example is that the user sets the MAC address and either (or both) the IPv4 and IPv6 addresses and masks on the device, and the PI header if needed. For example:

TapFdNetDeviceHelper helper;
helper.SetDeviceName (deviceName);
helper.SetModePi (modePi);
helper.SetTapIpv4Address (tapIp);
helper.SetTapIpv4Mask (tapMask);
...
helper.Install (node);
PlanetLabFdNetDeviceHelper

PlanetLab is a world wide distributed network testbed composed of nodes connected to the Internet. Running ns-3 simulations in PlanetLab nodes using the PlanetLabFdNetDeviceHelper allows to send simulated traffic generated by ns-3 directly to the Internet. This setup can be useful to validate ns-3 Internet protocols or other future protocols implemented in ns-3.

To run experiments using PlanetLab nodes it is required to have a PlanetLab account. Only members of PlanetLab partner institutions can obtain such accounts ( for more information visit http://www.planet-lab.org/ or http://www.planet-lab.eu ). Once the account is obtained, a PlanetLab slice must be requested in order to conduct experiments. A slice represents an experiment unit related to a group of PlanetLab users, and can be associated to virtual machines in different PlanetLab nodes. Slices can also be customized by adding configuration tags to it (this is done by PlanetLab administrators).

The PlanetLabFdNetDeviceHelper creates TAP devices on PlanetLab nodes using specific PlanetLab mechanisms (i.e. the vsys system), and associates the TAP device to a FdNetDevice in ns-3. The functionality provided by this helper is similar to that provided by the FdTapNetDeviceHelper, except that the underlying mechanisms to create the TAP device are different.

+-------------------------------------+
|         PlanetLab  host             |
+-------------------------------------+
|    ns-3 simulation   |              |
+----------------------+              |
|       ns-3 Node      |              |
|  +----------------+  |              |
|  |    ns-3 TCP    |  |              |
|  +----------------+  |              |
|  |    ns-3 IP     |  |              |
|  +----------------+  |              |
|  |   FdNetDevice  |  |              |
|--+----------------+--+    +------+  |
|       | TAP  |            | eth0 |  |
|       +------+            +------+  |
|     192.168.0.1               |     |
+-------------------------------|-----+
                                |
                                |
                                ------------ (Internet) -----

In order to be able to assign private IPv4 addresses to the TAP devices, account holders must request the vsys_vnet tag to be added to their slice by PlanetLab administrators. The vsys_vnet tag is associated to private network segment and only addresses from this segment can be used in experiments.

The syntax used to create a TAP device with this helper is similar to that used for the previously described helpers:

PlanetLabFdNetDeviceHelper helper;
helper.SetTapIpAddress (tapIp);
helper.SetTapMask (tapMask);
...
helper.Install (node);

PlanetLab nodes have a Fedora based distribution, so ns-3 can be installed following the instructions for ns-3 Linux installation.

Attributes

The FdNetDevice provides a number of attributes:

  • Address: The MAC address of the device

  • Start: The simulation start time to spin up the device thread

  • Stop: The simulation start time to stop the device thread

  • EncapsulationMode: Link-layer encapsulation format

  • RxQueueSize: The buffer size of the read queue on the file descriptor

    thread (default of 1000 packets)

Start and Stop do not normally need to be specified unless the user wants to limit the time during which this device is active. Address needs to be set to some kind of unique MAC address if the simulation will be interacting with other real devices somehow using real MAC addresses. Typical code:

device->SetAttribute ("Address", Mac48AddressValue (Mac48Address::Allocate ()));
Output

Ascii and PCAP tracing is provided similar to the other ns-3 NetDevice types, through the helpers, such as (e.g.):

::
EmuFdNetDeviceHelper emu; NetDeviceContainer devices = emu.Install (node); ... emu.EnablePcap (“emu-ping”, device, true);

The standard set of Mac-level NetDevice trace sources is provided.

  • MaxTx: Trace source triggered when ns-3 provides the device with a new frame to send
  • MaxTxDrop: Trace source if write to file descriptor fails
  • MaxPromiscRx: Whenever any valid Mac frame is received
  • MaxRx: Whenever a valid Mac frame is received for this device
  • Sniffer: Non-promiscuous packet sniffer
  • PromiscSniffer: Promiscuous packet sniffer (for tcpdump-like traces)
Examples

Several examples are provided:

  • dummy-network.cc: This simple example creates two nodes and interconnects them with a Unix pipe by passing the file descriptors from the socketpair into the FdNetDevice objects of the respective nodes.
  • realtime-dummy-network.cc: Same as dummy-network.cc but uses the real time simulator implementnation instead of the default one.
  • fd2fd-onoff.cc: This example is aimed at measuring the throughput of the FdNetDevice in a pure simulation. For this purpose two FdNetDevices, attached to different nodes but in a same simulation, are connected using a socket pair. TCP traffic is sent at a saturating data rate.
  • fd-emu-onoff.cc: This example is aimed at measuring the throughput of the FdNetDevice when using the EmuFdNetDeviceHelper to attach the simulated device to a real device in the host machine. This is achieved by saturating the channel with TCP traffic.
  • fd-emu-ping.cc: This example uses the EmuFdNetDeviceHelper to send ICMP traffic over a real channel.
  • fd-emu-udp-echo.cc: This example uses the EmuFdNetDeviceHelper to send UDP traffic over a real channel.
  • fd-planetlab-ping.cc: This example shows how to set up an experiment to send ICMP traffic from a PlanetLab node to the Internet.
  • fd-tap-ping.cc: This example uses the TapFdNetDeviceHelper to send ICMP traffic over a real channel.

Tap NetDevice

The Tap NetDevice can be used to allow a host system or virtual machines to interact with a simulation.

TapBridge Model Overview

The Tap Bridge is designed to integrate “real” internet hosts (or more precisely, hosts that support Tun/Tap devices) into ns-3 simulations. The goal is to make it appear to a “real” host node in that it has an ns-3 net device as a local device. The concept of a “real host” is a bit slippery since the “real host” may actually be virtualized using readily available technologies such as VMware, VirtualBox or OpenVZ.

Since we are, in essence, connecting the inputs and outputs of an ns-3 net device to the inputs and outputs of a Linux Tap net device, we call this arrangement a Tap Bridge.

There are three basic operating modes of this device available to users. Basic functionality is essentially identical, but the modes are different in details regarding how the arrangement is created and configured; and what devices can live on which side of the bridge.

We call these three modes the ConfigureLocal, UseLocal and UseBridge modes. The first “word” in the camel case mode identifier indicates who has the responsibility for creating and configuring the taps. For example, the “Configure” in ConfigureLocal mode indicates that it is the TapBridge that has responsibility for configuring the tap. In UseLocal mode and UseBridge modes, the “Use” prefix indicates that the TapBridge is asked to “Use” an existing configuration.

In other words, in ConfigureLocal mode, the TapBridge has the responsibility for creating and configuring the TAP devices. In UseBridge or UseLocal modes, the user provides a configuration and the TapBridge adapts to that configuration.

TapBridge ConfigureLocal Mode

In the ConfigureLocal mode, the configuration of the tap device is ns-3 configuration-centric. Configuration information is taken from a device in the ns-3 simulation and a tap device matching the ns-3 attributes is automatically created. In this case, a Linux computer is made to appear as if it was directly connected to a simulated ns-3 network.

This is illustrated below:

+--------+
|  Linux |
|  host  |                    +----------+
| ------ |                    |   ghost  |
|  apps  |                    |   node   |
| ------ |                    | -------- |
|  stack |                    |    IP    |     +----------+
| ------ |                    |   stack  |     |   node   |
|  TAP   |                    |==========|     | -------- |
| device | <----- IPC ------> |   tap    |     |    IP    |
+--------+                    |  bridge  |     |   stack  |
                              | -------- |     | -------- |
                              |   ns-3   |     |   ns-3   |
                              |   net    |     |   net    |
                              |  device  |     |  device  |
                              +----------+     +----------+
                                   ||               ||
                              +---------------------------+
                              |        ns-3 channel       |
                              +---------------------------+

In this case, the “ns-3 net device” in the “ghost node” appears as if it were actually replacing the TAP device in the Linux host. The ns-3 simulation creates the TAP device on the underlying Linux OS and configures the IP and MAC addresses of the TAP device to match the values assigned to the simulated ns-3 net device. The “IPC” link shown above is the network tap mechanism in the underlying OS. The whole arrangement acts as a conventional bridge; but a bridge between devices that happen to have the same shared MAC and IP addresses.

Here, the user is not required to provide any configuration information specific to the tap. A tap device will be created and configured by ns-3 according to its defaults, and the tap device will have its name assigned by the underlying operating system according to its defaults.

If the user has a requirement to access the created tap device, he or she may optionally provide a “DeviceName” attribute. In this case, the created OS tap device will be named accordingly.

The ConfigureLocal mode is the default operating mode of the Tap Bridge.

TapBridge UseLocal Mode

The UseLocal mode is quite similar to the ConfigureLocal mode. The significant difference is, as the mode name implies, the TapBridge is going to “Use” an existing tap device previously created and configured by the user. This mode is particularly useful when a virtualization scheme automatically creates tap devices and ns-3 is used to provide simulated networks for those devices.

+--------+
|  Linux |
|  host  |                    +----------+
| ------ |                    |   ghost  |
|  apps  |                    |   node   |
| ------ |                    | -------- |
|  stack |                    |    IP    |     +----------+
| ------ |                    |   stack  |     |   node   |
|  TAP   |                    |==========|     | -------- |
| device | <----- IPC ------> |   tap    |     |    IP    |
| MAC X  |                    |  bridge  |     |   stack  |
+--------+                    | -------- |     | -------- |
                              |   ns-3   |     |   ns-3   |
                              |   net    |     |   net    |
                              |  device  |     |  device  |
                              |  MAC Y   |     |  MAC Z   |
                              +----------+     +----------+
                                   ||               ||
                              +---------------------------+
                              |        ns-3 channel       |
                              +---------------------------+

In this case, the pre-configured MAC address of the “Tap device” (MAC X) will not be the same as that of the bridged “ns-3 net device” (MAC Y) shown in the illustration above. In order to bridge to ns-3 net devices which do not support SendFrom() (especially wireless STA nodes) we impose a requirement that only one Linux device (with one unique MAC address – here X) generates traffic that flows across the IPC link. This is because the MAC addresses of traffic across the IPC link will be “spoofed” or changed to make it appear to Linux and ns-3 that they have the same address. That is, traffic moving from the Linux host to the ns-3 ghost node will have its MAC address changed from X to Y and traffic from the ghost node to the Linux host will have its MAC address changed from Y to X. Since there is a one-to-one correspondence between devices, there may only be one MAC source flowing from the Linux side. This means that Linux bridges with more than one net device added are incompatible with UseLocal mode.

In UseLocal mode, the user is expected to create and configure a tap device completely outside the scope of the ns-3 simulation using something like:

$ sudo tunctl -t tap0
$ sudo ifconfig tap0 hw ether 08:00:2e:00:00:01
$ sudo ifconfig tap0 10.1.1.1 netmask 255.255.255.0 up

To tell the TapBridge what is going on, the user will set either directly into the TapBridge or via the TapBridgeHelper, the “DeviceName” attribute. In the case of the configuration above, the “DeviceName” attribute would be set to “tap0” and the “Mode” attribute would be set to “UseLocal”.

One particular use case for this mode is in the OpenVZ environment. There it is possible to create a Tap device on the “Hardware Node” and move it into a Virtual Private Server. If the TapBridge is able to use an existing tap device it is then possible to avoid the overhead of an OS bridge in that environment.

TapBridge UseBridge Mode

The simplest mode for those familiar with Linux networking is the UseBridge mode. Again, the “Use” prefix indicates that the TapBridge is going to Use an existing configuration. In this case, the TapBridge is going to logically extend a Linux bridge into ns-3.

This is illustrated below:

+---------+
|  Linux  |                             +----------+
| ------- |                             |   ghost  |
|  apps   |                             |   node   |
| ------- |                             | -------- |
|  stack  |                             |    IP    |     +----------+
| ------- | +--------+                  |   stack  |     |   node   |
| Virtual | |  TAP   |                  |==========|     | -------- |
| Device  | | Device | <---- IPC -----> |   tap    |     |    IP    |
+---------+ +--------+                  |  bridge  |     |   stack  |
    ||          ||                      | -------- |     | -------- |
+--------------------+                  |   ns-3   |     |   ns-3   |
| OS (brctl) Bridge  |                  |   net    |     |   net    |
+--------------------+                  |  device  |     |  device  |
                                        +----------+     +----------+
                                             ||               ||
                                        +---------------------------+
                                        |        ns-3 channel       |
                                        +---------------------------+

In this case, a computer running Linux applications, protocols, etc., is connected to a ns-3 simulated network in such a way as to make it appear to the Linux host that the TAP device is a real network device participating in the Linux bridge.

In the ns-3 simulation, a TapBridge is created to match each TAP Device. The name of the TAP Device is assigned to the Tap Bridge using the “DeviceName” attribute. The TapBridge then logically extends the OS bridge to encompass the ns-3 net device.

Since this mode logically extends an OS bridge, there may be many Linux net devices on the non-ns-3 side of the bridge. Therefore, like a net device on any bridge, the ns-3 net device must deal with the possibly of many source addresses. Thus, ns-3 devices must support SendFrom() (NetDevice::SupportsSendFrom() must return true) in order to be configured for use in UseBridge mode.

It is expected that the user will do something like the following to configure the bridge and tap completely outside ns-3:

$ sudo brctl addbr mybridge
$ sudo tunctl -t mytap
$ sudo ifconfig mytap hw ether 00:00:00:00:00:01
$ sudo ifconfig mytap 0.0.0.0 up
$ sudo brctl addif mybridge mytap
$ sudo brctl addif mybridge ...
$ sudo ifconfig mybridge 10.1.1.1 netmask 255.255.255.0 up

To tell the TapBridge what is going on, the user will set either directly into the TapBridge or via the TapBridgeHelper, the “DeviceName” attribute. In the case of the configuration above, the “DeviceName” attribute would be set to “mytap” and the “Mode” attribute would be set to “UseBridge”.

This mode is especially useful in the case of virtualization where the configuration of the virtual hosts may be dictated by another system and not be changable to suit ns-3. For example, a particular VM scheme may create virtual “vethx” or “vmnetx” devices that appear local to virtual hosts. In order to connect to such systems, one would need to manually create TAP devices on the host system and brigde these TAP devices to the existing (VM) virtual devices. The job of the Tap Bridge in this case is to extend the bridge to join a ns-3 net device.

TapBridge ConfigureLocal Operation

In ConfigureLocal mode, the TapBridge and therefore its associated ns-3 net device appears to the Linux host computer as a network device just like any arbitrary “eth0” or “ath0” might appear. The creation and configuration of the TAP device is done by the ns-3 simulation and no manual configuration is required by the user. The IP addresses, MAC addresses, gateways, etc., for created TAP devices are extracted from the simulation itself by querying the configuration of the ns-3 device and the TapBridge Attributes.

Since the MAC addresses are identical on the Linux side and the ns-3 side, we can use Send() on the ns-3 device which is available on all ns-3 net devices. Since the MAC addresses are identical there is no requirement to hook the promiscuous callback on the receive side. Therefore there are no restrictions on the kinds of net device that are usable in ConfigureLocal mode.

The TapBridge appears to an ns-3 simulation as a channel-less net device. This device must not have an IP address associated with it, but the bridged (ns-3) net device must have an IP address. Be aware that this is the inverse of an ns-3 BridgeNetDevice (or a conventional bridge in general) which demands that its bridge ports not have IP addresses, but allows the bridge device itself to have an IP address.

The host computer will appear in a simulation as a “ghost” node that contains one TapBridge for each NetDevice that is being bridged. From the perspective of a simulation, the only difference between a ghost node and any other node will be the presence of the TapBridge devices. Note however, that the presence of the TapBridge does affect the connectivity of the net device to the IP stack of the ghost node.

Configuration of address information and the ns-3 devices is not changed in any way if a TapBridge is present. A TapBridge will pick up the addressing information from the ns-3 net device to which it is connected (its “bridged” net device) and use that information to create and configure the TAP device on the real host.

The end result of this is a situation where one can, for example, use the standard ping utility on a real host to ping a simulated ns-3 node. If correct routes are added to the internet host (this is expected to be done automatically in future ns-3 releases), the routing systems in ns-3 will enable correct routing of the packets across simulated ns-3 networks. For an example of this, see the example program, tap-wifi-dumbbell.cc in the ns-3 distribution.

The Tap Bridge lives in a kind of a gray world somewhere between a Linux host and an ns-3 bridge device. From the Linux perspective, this code appears as the user mode handler for a TAP net device. In ConfigureLocal mode, this Tap device is automatically created by the ns-3 simulation. When the Linux host writes to one of these automatically created /dev/tap devices, the write is redirected into the TapBridge that lives in the ns-3 world; and from this perspective, the packet write on Linux becomes a packet read in the Tap Bridge. In other words, a Linux process writes a packet to a tap device and this packet is redirected by the network tap mechanism toan ns-3 process where it is received by the TapBridge as a result of a read operation there. The TapBridge then writes the packet to the ns-3 net device to which it is bridged; and therefore it appears as if the Linux host sent a packet directly through an ns-3 net device onto an ns-3 network.

In the other direction, a packet received by the ns-3 net device connected to the Tap Bridge is sent via a receive callback to the TapBridge. The TapBridge then takes that packet and writes it back to the host using the network tap mechanism. This write to the device will appear to the Linux host as if a packet has arrived on a net device; and therefore as if a packet received by the ns-3 net device during a simulation has appeared on a real Linux net device.

The upshot is that the Tap Bridge appears to bridge a tap device on a Linux host in the “real world” to an ns-3 net device in the simulation. Because the TAP device and the bridged ns-3 net device have the same MAC address and the network tap IPC link is not externalized, this particular kind of bridge makes it appear that a ns-3 net device is actually installed in the Linux host.

In order to implement this on the ns-3 side, we need a “ghost node” in the simulation to hold the bridged ns-3 net device and the TapBridge. This node should not actually do anything else in the simulation since its job is simply to make the net device appear in Linux. This is not just arbitrary policy, it is because:

  • Bits sent to the TapBridge from higher layers in the ghost node (using the TapBridge Send method) are completely ignored. The TapBridge is not, itself, connected to any network, neither in Linux nor in ns-3. You can never send nor receive data over a TapBridge from the ghost node.
  • The bridged ns-3 net device has its receive callback disconnected from the ns-3 node and reconnected to the Tap Bridge. All data received by a bridged device will then be sent to the Linux host and will not be received by the node. From the perspective of the ghost node, you can send over this device but you cannot ever receive.

Of course, if you understand all of the issues you can take control of your own destiny and do whatever you want – we do not actively prevent you from using the ghost node for anything you decide. You will be able to perform typical ns-3 operations on the ghost node if you so desire. The internet stack, for example, must be there and functional on that node in order to participate in IP address assignment and global routing. However, as mentioned above, interfaces talking to any TapBridge or associated bridged net devices will not work completely. If you understand exactly what you are doing, you can set up other interfaces and devices on the ghost node and use them; or take advantage of the operational send side of the bridged devices to create traffic generators. We generally recommend that you treat this node as a ghost of the Linux host and leave it to itself, though.

TapBridge UseLocal Mode Operation

As described in above, the TapBridge acts like a bridge from the “real” world into the simulated ns-3 world. In the case of the ConfigureLocal mode, life is easy since the IP address of the Tap device matches the IP address of the ns-3 device and the MAC address of the Tap device matches the MAC address of the ns-3 device; and there is a one-to-one relationship between the devices.

Things are slightly complicated when a Tap device is externally configured with a different MAC address than the ns-3 net device. The conventional way to deal with this kind of difference is to use promiscuous mode in the bridged device to receive packets destined for the different MAC address and forward them off to Linux. In order to move packets the other way, the conventional solution is SendFrom() which allows a caller to “spoof” or change the source MAC address to match the different Linux MAC address.

We do have a specific requirement to be able to bridge Linux Virtual Machines onto wireless STA nodes. Unfortunately, the 802.11 spec doesn’t provide a good way to implement SendFrom(), so we have to work around that problem.

To this end, we provided the UseLocal mode of the Tap Bridge. This mode allows you approach the problem as if you were creating a bridge with a single net device. A single allowed address on the Linux side is remembered in the TapBridge, and all packets coming from the Linux side are repeated out the ns-3 side using the ns-3 device MAC source address. All packets coming in from the ns-3 side are repeated out the Linux side using the remembered MAC address. This allows us to use Send() on the ns-3 device side which is available on all ns-3 net devices.

UseLocal mode is identical to the ConfigureLocal mode except for the creation and configuration of the tap device and the MAC address spoofing.

TapBridge UseBridge Operation

As described in the ConfigureLocal mode section, when the Linux host writes to one of the /dev/tap devices, the write is redirected into the TapBridge that lives in the ns-3 world. In the case of the UseBridge mode, these packets will need to be sent out on the ns-3 network as if they were sent on a device participating in the Linux bridge. This means calling the SendFrom() method on the bridged device and providing the source MAC address found in the packet.

In the other direction, a packet received by an ns-3 net device is hooked via callback to the TapBridge. This must be done in promiscuous mode since the goal is to bridge the ns-3 net device onto the OS (brctl) bridge of which the TAP device is a part.

For these reasons, only ns-3 net devices that support SendFrom() and have a hookable promiscuous receive callback are allowed to participate in UseBridge mode TapBridge configurations.

Tap Bridge Channel Model

There is no channel model associated with the Tap Bridge. In fact, the intention is make it appear that the real internet host is connected to the channel of the bridged net device.

Tap Bridge Tracing Model

Unlike most ns-3 devices, the TapBridge does not provide any standard trace sources. This is because the bridge is an intermediary that is essentially one function call away from the bridged device. We expect that the trace hooks in the bridged device will be sufficient for most users,

Using the TapBridge

We expect that most users will interact with the TapBridge device through the TapBridgeHelper. Users of other helper classes, such as CSMA or Wifi, should be comfortable with the idioms used there.

Energy Framework

Energy consumption is a key issue for wireless devices, and wireless network researchers often need to investigate the energy consumption at a node or in the overall network while running network simulations in ns-3. This requires ns-3 to support energy consumption modeling. Further, as concepts such as fuel cells and energy scavenging are becoming viable for low power wireless devices, incorporating the effect of these emerging technologies into simulations requires support for modeling diverse energy sources in ns-3. The ns-3 Energy Framework provides the basis for energy consumption and energy source modeling.

Model Description

The source code for the Energy Framework is currently at: src/energy.

Design

The ns-3 Energy Framework is composed of 2 parts: Energy Source and Device Energy Model. The framework will be implemented into the src/energy/models folder.

Energy Source

The Energy Source represents the power supply on each node. A node can have one or more energy sources, and each energy source can be connected to multiple device energy models. Connecting an energy source to a device energy model implies that the corresponding device draws power from the source. The basic functionality of the Energy Source is to provide energy for devices on the node. When energy is completely drained from the Energy Source, it notifies the devices on node such that each device can react to this event. Further, each node can access the Energy Source Objects for information such as remaining energy or energy fraction (battery level). This enables the implementation of energy aware protocols in ns-3.

In order to model a wide range of power supplies such as batteries, the Energy Source must be able to capture characteristics of these supplies. There are 2 important characteristics or effects related to practical batteries:

  • Rate Capacity Effect: Decrease of battery lifetime when the current draw is higher than the rated value of the battery.
  • Recovery Effect: Increase of battery lifetime when the battery is alternating between discharge and idle states.

In order to incorporate the Rate Capacity Effect, the Energy Source uses current draw from all devices on the same node to calculate energy consumption. The Energy Source polls all devices on the same node periodically to calculate the total current draw and hence the energy consumption. When a device changes state, its corresponding Device Energy Model will notify the Energy Source of this change and new total current draw will be calculated.

The Energy Source base class keeps a list of devices (Device Energy Model objects) using the particular Energy Source as power supply. When energy is completely drained, the Energy Source will notify all devices on this list. Each device can then handle this event independently, based on the desired behavior when power supply is drained.

Device Energy Model

The Device Energy Model is the energy consumption model of a device on node. It is designed to be a state based model where each device is assumed to have a number of states, and each state is associated with a power consumption value. Whenever the state of the device changes, the corresponding Device Energy Model will notify the Energy Source of the new current draw of the device. The Energy Source will then calculate the new total current draw and update the remaining energy.

The Device Energy Model can also be used for devices that do not have finite number of states. For example, in an electric vehicle, the current draw of the motor is determined by its speed. Since the vehicle’s speed can take continuous values within a certain range, it is infeasible to define a set of discrete states of operation. However, by converting the speed value into current directly, the same set of Device Energy Model APIs can still be used.

Future Work

For Device Energy Models, we are planning to include support for other PHY layer models provided in ns-3 such as WiMAX. For Energy Sources, we are planning to included new types of Energy Sources such as energy scavenging.

References

[1]ns-2 Energy model: http://www.cubinlab.ee.unimelb.edu.au/~jrid/Docs/Manuel-NS2/node204.html
[2]
  1. Wu, S. Nabar and R. Poovendran. An Energy Framework for the Network Simulator 3 (ns-3).
[3]M. Handy and D. Timmermann. Simulation of mobile wireless networks with accurate modelling of non-linear battery effects. In Proc. of Applied simulation and Modeling (ASM), 2003.
[4]D. N. Rakhmatov and S. B. Vrudhula. An analytical high-level battery model for use in energy management of portable electronic systems. In Proc. of IEEE/ACM International Conference on Computer Aided Design (ICCAD’01), pages 488–493, November 2001.
[5]D. N. Rakhmatov, S. B. Vrudhula, and D. A. Wallach. Battery lifetime prediction for energy-aware computing. In Proc. of the 2002 International Symposium on Low Power Electronics and Design (ISLPED’02), pages 154–159, 2002.

Usage

The main way that ns-3 users will typically interact with the Energy Framework is through the helper API and through the publicly visible attributes of the framework. The helper API is defined in src/energy/helper/*.h.

In order to use the energy framework, the user must install an Energy Source for the node of interest and the corresponding Device Energy Model for the network devices. Energy Source (objects) are aggregated onto each node by the Energy Source Helper. In order to allow multiple energy sources per node, we aggregate an Energy Source Container rather than directly aggregating a source object.

The Energy Source object also keeps a list of Device Energy Model objects using the source as power supply. Device Energy Model objects are installed onto the Energy Source by the Device Energy Model Helper. User can access the Device Energy Model objects through the Energy Source object to obtain energy consumption information of individual devices.

Examples

The example directories, src/examples/energy and examples/energy, contain some basic code that shows how to set up the framework.

Helpers

Energy Source Helper

Base helper class for Energy Source objects, this helper Aggregates Energy Source object onto a node. Child implementation of this class creates the actual Energy Source object.

Device Energy Model Helper

Base helper class for Device Energy Model objects, this helper attaches Device Energy Model objects onto Energy Source objects. Child implementation of this class creates the actual Device Energy Model object.

Attributes

Attributes differ between Energy Sources and Devices Energy Models implementations, please look at the specific child class for details.

Basic Energy Source
  • BasicEnergySourceInitialEnergyJ: Initial energy stored in basic energy source.
  • BasicEnergySupplyVoltageV: Initial supply voltage for basic energy source.
  • PeriodicEnergyUpdateInterval: Time between two consecutive periodic energy updates.
RV Battery Model
  • RvBatteryModelPeriodicEnergyUpdateInterval: RV battery model sampling interval.
  • RvBatteryModelOpenCircuitVoltage: RV battery model open circuit voltage.
  • RvBatteryModelCutoffVoltage: RV battery model cutoff voltage.
  • RvBatteryModelAlphaValue: RV battery model alpha value.
  • RvBatteryModelBetaValue: RV battery model beta value.
  • RvBatteryModelNumOfTerms: The number of terms of the infinite sum for estimating battery level.
WiFi Radio Energy Model
  • IdleCurrentA: The default radio Idle current in Ampere.
  • CcaBusyCurrentA: The default radio CCA Busy State current in Ampere.
  • TxCurrentA: The radio Tx current in Ampere.
  • RxCurrentA: The radio Rx current in Ampere.
  • SwitchingCurrentA: The default radio Channel Switch current in Ampere.

Tracing

Traced values differ between Energy Sources and Devices Energy Models implementations, please look at the specific child class for details.

Basic Energy Source
  • RemainingEnergy: Remaining energy at BasicEnergySource.
RV Battery Model
  • RvBatteryModelBatteryLevel: RV battery model battery level.
  • RvBatteryModelBatteryLifetime: RV battery model battery lifetime.
WiFi Radio Energy Model
  • TotalEnergyConsumption: Total energy consumption of the radio device.

Validation

Comparison of the Energy Framework against actual devices have not been performed. Current implementation of the Energy Framework is checked numerically for computation errors. The RV battery model is validated by comparing results with what was presented in the original RV battery model paper.

Flow Monitor

Model Description

The source code for the new module lives in the directory src/flow-monitor.

The Flow Monitor module goal is to provide a flexible system to measure the performance of network protocols. The module uses probes, installed in network nodes, to track the packets exchanged by the nodes, and it will measure a number of parameters. Packets are divided according to the flow they belong to, where each flow is defined according to the probe’s characteristics (e.g., for IP, a flow is defined as the packets with the same {protocol, source (IP, port), destination (IP, port)} tuple.

The statistics are collected for each flow can be exported in XML format. Moreover, the user can access the probes directly to request specific stats about each flow.

Design

Flow Monitor module is designed in a modular way. It can be extended by subclassing ns3::FlowProbe and ns3::FlowClassifier.

The full module design is described in [FlowMonitor]

Scope and Limitations

At the moment, probes and classifiers are available for IPv4 and IPv6.

Each probe will classify packets in four points:

  • When a packet is sent (SendOutgoing IPv[4,6] traces)
  • When a packet is forwarded (UnicastForward IPv[4,6] traces)
  • When a packet is received (LocalDeliver IPv[4,6] traces)
  • When a packet is dropped (Drop IPv[4,6] traces)

Since the packets are tracked at IP level, any retransmission caused by L4 protocols (e.g., TCP) will be seen by the probe as a new packet.

A Tag will be added to the packet (ns3::Ipv[4,6]FlowProbeTag). The tag will carry basic packet’s data, useful for the packet’s classification.

It must be underlined that only L4 (TCP, UDP) packets are, so far, classified. Moreover, only unicast packets will be classified. These limitations may be removed in the future.

The data collected for each flow are:

  • timeFirstTxPacket: when the first packet in the flow was transmitted;
  • timeLastTxPacket: when the last packet in the flow was transmitted;
  • timeFirstRxPacket: when the first packet in the flow was received by an end node;
  • timeLastRxPacket: when the last packet in the flow was received;
  • delaySum: the sum of all end-to-end delays for all received packets of the flow;
  • jitterSum: the sum of all end-to-end delay jitter (delay variation) values for all received packets of the flow, as defined in RFC 3393;
  • txBytes, txPackets: total number of transmitted bytes / packets for the flow;
  • rxBytes, rxPackets: total number of received bytes / packets for the flow;
  • lostPackets: total number of packets that are assumed to be lost (not reported over 10 seconds);
  • timesForwarded: the number of times a packet has been reportedly forwarded;
  • delayHistogram, jitterHistogram, packetSizeHistogram: histogram versions for the delay, jitter, and packet sizes, respectively;
  • packetsDropped, bytesDropped: the number of lost packets and bytes, divided according to the loss reason code (defined in the probe).

It is worth pointing out that the probes measure the packet bytes including IP headers. The L2 headers are not included in the measure.

These stats will be written in XML form upon request (see the Usage section).

References

[FlowMonitor]
  1. Carneiro, P. Fortuna, and M. Ricardo. 2009. FlowMonitor: a network monitoring framework for the network simulator 3 (NS-3). In Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS ‘09). http://dx.doi.org/10.4108/ICST.VALUETOOLS2009.7493

Usage

The module usage is extremely simple. The helper will take care of about everything.

The typical use is:

// Flow monitor
Ptr<FlowMonitor> flowMonitor;
FlowMonitorHelper flowHelper;
flowMonitor = flowHelper.InstallAll();

Simulator::Stop (Seconds(stop_time));
Simulator::Run ();

flowMonitor->SerializeToXmlFile("NameOfFile.xml", true, true);

the SerializeToXmlFile () function 2nd and 3rd parameters are used respectively to activate/deactivate the histograms and the per-probe detailed stats.

Other possible alternatives can be found in the Doxygen documentation.

Helpers

The helper API follows the pattern usage of normal helpers. Through the helper you can install the monitor in the nodes, set the monitor attributes, and print the statistics.

One important thing is: the ns3::FlowMonitorHelper must be instantiated only once in the main.

Attributes

The module provides the following attributes in ns3::FlowMonitor:

  • MaxPerHopDelay (Time, default 10s): The maximum per-hop delay that should be considered;
  • StartTime (Time, default 0s): The time when the monitoring starts;
  • DelayBinWidth (double, default 0.001): The width used in the delay histogram;
  • JitterBinWidth (double, default 0.001): The width used in the jitter histogram;
  • PacketSizeBinWidth (double, default 20.0): The width used in the packetSize histogram;
  • FlowInterruptionsBinWidth (double, default 0.25): The width used in the flowInterruptions histogram;
  • FlowInterruptionsMinTime (double, default 0.5): The minimum inter-arrival time that is considered a flow interruption.

Output

The main model output is an XML formatted report about flow statistics. An example is:

<?xml version="1.0" ?>
<FlowMonitor>
  <FlowStats>
  <Flow flowId="1" timeFirstTxPacket="+0.0ns" timeFirstRxPacket="+20067198.0ns" timeLastTxPacket="+2235764408.0ns" timeLastRxPacket="+2255831606.0ns" delaySum="+138731526300.0ns" jitterSum="+1849692150.0ns" lastDelay="+20067198.0ns" txBytes="2149400" rxBytes="2149400" txPackets="3735" rxPackets="3735" lostPackets="0" timesForwarded="7466">
  </Flow>
  </FlowStats>
  <Ipv4FlowClassifier>
  <Flow flowId="1" sourceAddress="10.1.3.1" destinationAddress="10.1.2.2" protocol="6" sourcePort="49153" destinationPort="50000" />
  </Ipv4FlowClassifier>
  <Ipv6FlowClassifier>
  </Ipv6FlowClassifier>
  <FlowProbes>
  <FlowProbe index="0">
    <FlowStats  flowId="1" packets="3735" bytes="2149400" delayFromFirstProbeSum="+0.0ns" >
    </FlowStats>
  </FlowProbe>
  <FlowProbe index="2">
    <FlowStats  flowId="1" packets="7466" bytes="2224020" delayFromFirstProbeSum="+199415389258.0ns" >
    </FlowStats>
  </FlowProbe>
  <FlowProbe index="4">
    <FlowStats  flowId="1" packets="3735" bytes="2149400" delayFromFirstProbeSum="+138731526300.0ns" >
    </FlowStats>
  </FlowProbe>
  </FlowProbes>
</FlowMonitor>

The output was generated by a TCP flow from 10.1.3.1 to 10.1.2.2.

It is worth noticing that the index 2 probe is reporting more packets and more bytes than the other probles. That’s a perfectly normal behaviour, as packets are fragmented at IP level in that node.

It should also be observed that the receiving node’s probe (index 4) doesn’t count the fragments, as the reassembly is done before the probing point.

Examples

The examples are located in src/flow-monitor/examples.

Moreover, the following examples use the flow-monitor module:

  • examples/matrix-topology/matrix-topology.cc
  • examples/routing/manet-routing-compare.cc
  • examples/routing/simple-global-routing.cc
  • examples/tcp/tcp-variants-comparison.cc
  • examples/wireless/multirate.cc
  • examples/wireless/wifi-hidden-terminal.cc

Troubleshooting

Do not define more than one ns3::FlowMonitorHelper in the simulation.

Validation

The paper in the references contains a full description of the module validation against a test network.

Tests are provided to ensure the Histogram correct functionality.

Internet Models

Internet Stack

Internet stack aggregation

A bare class Node is not very useful as-is; other objects must be aggregated to it to provide useful node functionality.

The ns-3 source code directory src/internet provides implementation of TCP/IPv4- and IPv6-related components. These include IPv4, ARP, UDP, TCP, IPv6, Neighbor Discovery, and other related protocols.

Internet Nodes are not subclasses of class Node; they are simply Nodes that have had a bunch of IP-related objects aggregated to them. They can be put together by hand, or via a helper function InternetStackHelper::Install () which does the following to all nodes passed in as arguments:

void
InternetStackHelper::Install (Ptr<Node> node) const
{
  if (m_ipv4Enabled)
    {
      /* IPv4 stack */
      if (node->GetObject<Ipv4> () != 0)
        {
          NS_FATAL_ERROR ("InternetStackHelper::Install (): Aggregating "
                          "an InternetStack to a node with an existing Ipv4 object");
          return;
        }

      CreateAndAggregateObjectFromTypeId (node, "ns3::ArpL3Protocol");
      CreateAndAggregateObjectFromTypeId (node, "ns3::Ipv4L3Protocol");
      CreateAndAggregateObjectFromTypeId (node, "ns3::Icmpv4L4Protocol");
      // Set routing
      Ptr<Ipv4> ipv4 = node->GetObject<Ipv4> ();
      Ptr<Ipv4RoutingProtocol> ipv4Routing = m_routing->Create (node);
      ipv4->SetRoutingProtocol (ipv4Routing);
    }

  if (m_ipv6Enabled)
    {
      /* IPv6 stack */
      if (node->GetObject<Ipv6> () != 0)
        {
          NS_FATAL_ERROR ("InternetStackHelper::Install (): Aggregating "
                          "an InternetStack to a node with an existing Ipv6 object");
          return;
        }

      CreateAndAggregateObjectFromTypeId (node, "ns3::Ipv6L3Protocol");
      CreateAndAggregateObjectFromTypeId (node, "ns3::Icmpv6L4Protocol");
      // Set routing
      Ptr<Ipv6> ipv6 = node->GetObject<Ipv6> ();
      Ptr<Ipv6RoutingProtocol> ipv6Routing = m_routingv6->Create (node);
      ipv6->SetRoutingProtocol (ipv6Routing);

      /* register IPv6 extensions and options */
      ipv6->RegisterExtensions ();
      ipv6->RegisterOptions ();
    }

  if (m_ipv4Enabled || m_ipv6Enabled)
    {
      /* UDP and TCP stacks */
      CreateAndAggregateObjectFromTypeId (node, "ns3::UdpL4Protocol");
      node->AggregateObject (m_tcpFactory.Create<Object> ());
      Ptr<PacketSocketFactory> factory = CreateObject<PacketSocketFactory> ();
      node->AggregateObject (factory);
    }
}

Where multiple implementations exist in ns-3 (TCP, IP routing), these objects are added by a factory object (TCP) or by a routing helper (m_routing).

Note that the routing protocol is configured and set outside this function. By default, the following protocols are added:

void InternetStackHelper::Initialize ()
{
  SetTcp ("ns3::TcpL4Protocol");
  Ipv4StaticRoutingHelper staticRouting;
  Ipv4GlobalRoutingHelper globalRouting;
  Ipv4ListRoutingHelper listRouting;
  Ipv6ListRoutingHelper listRoutingv6;
  Ipv6StaticRoutingHelper staticRoutingv6;
  listRouting.Add (staticRouting, 0);
  listRouting.Add (globalRouting, -10);
  listRoutingv6.Add (staticRoutingv6, 0);
  SetRoutingHelper (listRouting);
  SetRoutingHelper (listRoutingv6);
}

By default, IPv4 and IPv6 are enabled.

Internet Node structure

An IP-capable Node (an ns-3 Node augmented by aggregation to have one or more IP stacks) has the following internal structure.

Layer-3 protocols

At the lowest layer, sitting above the NetDevices, are the “layer 3” protocols, including IPv4, IPv6, ARP and so on. The class Ipv4L3Protocol is an implementation class whose public interface is typically class Ipv4, but the Ipv4L3Protocol public API is also used internally at present.

In class Ipv4L3Protocol, one method described below is Receive ():

/**
  * Lower layer calls this method after calling L3Demux::Lookup
  * The ARP subclass needs to know from which NetDevice this
  * packet is coming to:
  *    - implement a per-NetDevice ARP cache
  *    - send back arp replies on the right device
  */
void Receive( Ptr<NetDevice> device, Ptr<const Packet> p, uint16_t protocol,
const Address &from, const Address &to, NetDevice::PacketType packetType);

First, note that the Receive () function has a matching signature to the ReceiveCallback in the class Node. This function pointer is inserted into the Node’s protocol handler when AddInterface () is called. The actual registration is done with a statement such as follows:

RegisterProtocolHandler ( MakeCallback (&Ipv4Protocol::Receive, ipv4),
                          Ipv4L3Protocol::PROT_NUMBER, 0);

The Ipv4L3Protocol object is aggregated to the Node; there is only one such Ipv4L3Protocol object. Higher-layer protocols that have a packet to send down to the Ipv4L3Protocol object can call GetObject<Ipv4L3Protocol> () to obtain a pointer, as follows:

Ptr<Ipv4L3Protocol> ipv4 = m_node->GetObject<Ipv4L3Protocol> ();
if (ipv4 != 0)
  {
    ipv4->Send (packet, saddr, daddr, PROT_NUMBER);
  }

This class nicely demonstrates two techniques we exploit in ns-3 to bind objects together: callbacks, and object aggregation.

Once IPv4 routing has determined that a packet is for the local node, it forwards it up the stack. This is done with the following function:

void
Ipv4L3Protocol::LocalDeliver (Ptr<const Packet> packet, Ipv4Header const&ip, uint32_t iif)

The first step is to find the right Ipv4L4Protocol object, based on IP protocol number. For instance, TCP is registered in the demux as protocol number 6. Finally, the Receive() function on the Ipv4L4Protocol (such as TcpL4Protocol::Receive is called.

We have not yet introduced the class Ipv4Interface. Basically, each NetDevice is paired with an IPv4 representation of such device. In Linux, this class Ipv4Interface roughly corresponds to the struct in_device; the main purpose is to provide address-family specific information (addresses) about an interface.

All the classes have appropriate traces in order to track sent, received and lost packets. The users is encouraged to use them so to find out if (and where) a packet is dropped. A common mistake is to forget the effects of local queues when sending packets, e.g., the ARP queue. This can be particularly puzzling when sending jumbo packets or packet bursts using UDP. The ARP cache pending queue is limited (3 datagrams) and IP packets might be fragmented, easily overfilling the ARP cache queue size. In those cases it is useful to increase the ARP cache pending size to a proper value, e.g.:

Config::SetDefault ("ns3::ArpCache::PendingQueueSize", UintegerValue (MAX_BURST_SIZE/L2MTU*3));

The IPv6 implementation follows a similar architecture. Dual-stacked nodes (one with support for both IPv4 and IPv6) will allow an IPv6 socket to receive IPv4 connections as a standard dual-stacked system does. A socket bound and listening to an IPv6 endpoint can receive an IPv4 connection and will return the remote address as an IPv4-mapped address. Support for the IPV6_V6ONLY socket option does not currently exist.

Layer-4 protocols and sockets

We next describe how the transport protocols, sockets, and applications tie together. In summary, each transport protocol implementation is a socket factory. An application that needs a new socket

For instance, to create a UDP socket, an application would use a code snippet such as the following:

Ptr<Udp> udpSocketFactory = GetNode ()->GetObject<Udp> ();
Ptr<Socket> m_socket = socketFactory->CreateSocket ();
m_socket->Bind (m_local_address);
...

The above will query the node to get a pointer to its UDP socket factory, will create one such socket, and will use the socket with an API similar to the C-based sockets API, such as Connect () and Send (). The address passed to the Bind (), Connect (), or Send () functions may be a Ipv4Address, Ipv6Address, or Address. If a Address is passed in and contains anything other than a Ipv4Address or Ipv6Address, these functions will return an error. The Bind (void) and Bind6 (void) functions bind to “0.0.0.0” and ”::” respectively. See the chapter on ns-3 sockets for more information.

We have described so far a socket factory (e.g. class Udp) and a socket, which may be specialized (e.g., class UdpSocket). There are a few more key objects that relate to the specialized task of demultiplexing a packet to one or more receiving sockets. The key object in this task is class Ipv4EndPointDemux. This demultiplexer stores objects of class Ipv4EndPoint. This class holds the addressing/port tuple (local port, local address, destination port, destination address) associated with the socket, and a receive callback. This receive callback has a receive function registered by the socket. The Lookup () function to Ipv4EndPointDemux returns a list of Ipv4EndPoint objects (there may be a list since more than one socket may match the packet). The layer-4 protocol copies the packet to each Ipv4EndPoint and calls its ForwardUp () method, which then calls the Receive () function registered by the socket.

An issue that arises when working with the sockets API on real systems is the need to manage the reading from a socket, using some type of I/O (e.g., blocking, non-blocking, asynchronous, ...). ns-3 implements an asynchronous model for socket I/O; the application sets a callback to be notified of received data ready to be read, and the callback is invoked by the transport protocol when data is available. This callback is specified as follows:

void Socket::SetRecvCallback (Callback<void, Ptr<Socket>,
                              Ptr<Packet>,
                              const Address&> receivedData);

The data being received is conveyed in the Packet data buffer. An example usage is in class PacketSink:

m_socket->SetRecvCallback (MakeCallback(&PacketSink::HandleRead, this));

To summarize, internally, the UDP implementation is organized as follows:

  • a UdpImpl class that implements the UDP socket factory functionality
  • a UdpL4Protocol class that implements the protocol logic that is socket-independent
  • a UdpSocketImpl class that implements socket-specific aspects of UDP
  • a class called Ipv4EndPoint that stores the addressing tuple (local port, local address, destination port, destination address) associated with the socket, and a receive callback for the socket.
IP-capable node interfaces

Many of the implementation details, or internal objects themselves, of IP-capable Node objects are not exposed at the simulator public API. This allows for different implementations; for instance, replacing the native ns-3 models with ported TCP/IP stack code.

The C++ public APIs of all of these objects is found in the src/network directory, including principally:

  • address.h
  • socket.h
  • node.h
  • packet.h

These are typically base class objects that implement the default values used in the implementation, implement access methods to get/set state variables, host attributes, and implement publicly-available methods exposed to clients such as CreateSocket.

Example path of a packet

These two figures show an example stack trace of how packets flow through the Internet Node objects.

_images/internet-node-send.png

Send path of a packet.

_images/internet-node-recv.png

Receive path of a packet.

IPv4

Placeholder chapter

IPv6

This chapter describes the ns-3 IPv6 model capabilities and limitations along with its usage and examples.

IPv6 model description

The IPv6 model is loosely patterned after the Linux implementation; the implementation is not complete as some features of IPv6 are not of much interest to simulation studies, and some features of IPv6 are simply not modeled yet in ns-3.

The base class Ipv6 defines a generic API, while the class Ipv6L3Protocol is the actual class implementing the protocol. The actual classes used by the IPv6 stack are located mainly in the directory src/internet.

The implementation of IPv6 is contained in the following files:

src/internet/model/icmpv6-header.{cc,h}
src/internet/model/icmpv6-l4-protocol.{cc,h}
src/internet/model/ipv6.{cc,h}
src/internet/model/ipv6-address-generator.{cc,h}
src/internet/model/ipv6-autoconfigured-prefix.{cc,h}
src/internet/model/ipv6-end-point.{cc,h}
src/internet/model/ipv6-end-point-demux.{cc,h}
src/internet/model/ipv6-extension.{cc,h}
src/internet/model/ipv6-extension-demux.{cc,h}
src/internet/model/ipv6-extension-header.{cc,h}
src/internet/model/ipv6-header.{cc,h}
src/internet/model/ipv6-interface.{cc,h}
src/internet/model/ipv6-interface-address.{cc,h}
src/internet/model/ipv6-l3-protocol.{cc,h}
src/internet/model/ipv6-list-routing.{cc,h}
src/internet/model/ipv6-option.{cc,h}
src/internet/model/ipv6-option-demux.{cc,h}
src/internet/model/ipv6-option-header.{cc,h}
src/internet/model/ipv6-packet-info-tag.{cc,h}
src/internet/model/ipv6-pmtu-cache.{cc,h}
src/internet/model/ipv6-raw-socket-factory.{cc,h}
src/internet/model/ipv6-raw-socket-factory-impl.{cc,h}
src/internet/model/ipv6-raw-socket-impl.{cc,h}
src/internet/model/ipv6-route.{cc,h}
src/internet/model/ipv6-routing-protocol.{cc,h}
src/internet/model/ipv6-routing-table-entry.{cc,h}
src/internet/model/ipv6-static-routing.{cc,h}
src/internet/model/ndisc-cache.{cc,h}
src/network/utils/inet6-socket-address.{cc,h}
src/network/utils/ipv6-address.{cc,h}

Also some helpers are involved with IPv6:

src/internet/helper/internet-stack-helper.{cc,h}
src/internet/helper/ipv6-address-helper.{cc,h}
src/internet/helper/ipv6-interface-container.{cc,h}
src/internet/helper/ipv6-list-routing-helper.{cc,h}
src/internet/helper/ipv6-routing-helper.{cc,h}
src/internet/helper/ipv6-static-routing-helper.{cc,h}

The model files can be roughly divided into:

  • protocol models (e.g., ipv6, ipv6-l3-protocol, icmpv6-l4-protocol, etc.)
  • routing models (i.e., anything with ‘routing’ in its name)
  • sockets and interfaces (e.g., ipv6-raw-socket, ipv6-interface, ipv6-end-point, etc.)
  • address-related things
  • headers, option headers, extension headers, etc.
  • accessory classes (e.g., ndisc-cache)

Usage

The following description is based on using the typical helpers found in the example code.

IPv6 does not need to be activated in a node. it is automatically added to the available protocols once the Internet Stack is installed.

In order to not install IPv6 along with IPv4, it is possible to use ns3::InternetStackHelper method SetIpv6StackInstall (bool enable) before installing the InternetStack in the nodes.

Note that to have an IPv6-only network (i.e., to not install the IPv4 stack in a node) one should use ns3::InternetStackHelper method SetIpv4StackInstall (bool enable) before the stack installation.

As an example, in the following code node 0 will have both IPv4 and IPv6, node 1 only only IPv6 and node 2 only IPv4:

NodeContainer n;
n.Create (3);

InternetStackHelper internet;
InternetStackHelper internetV4only;
InternetStackHelper internetV6only;

internetV4only.SetIpv6StackInstall (false);
internetV6only.SetIpv4StackInstall (false);

internet.Install (n.Get (0));
internetV6only.Install (n.Get (1));
internetV4only.Install (n.Get (2));
IPv6 addresses assignment

In order to use IPv6 on a network, the first thing to do is assigning IPv6 addresses.

Any IPv6-enabled ns-3 node will have at least one NetDevice: the ns3::LoopbackNetDevice. The loopback device address is ::1. All the other NetDevices will have one or more IPv6 addresses:

  • One link-local address: fe80::interface ID, where interface ID is derived from the NetDevice MAC address.
  • Zero or more global addresses, e.g., 2001:db8::1.

Typically the first address on an interface will be the link-local one, with the global address(es) being the following ones.

IPv6 global addresses might be:

  • manually assigned
  • auto-generated

ns-3 can use both methods, and it’s quite important to understand the implications of both.

Manually assigned IPv6 adddresses

This is probably the easiest and most used method. As an example:

Ptr<Node> n0 = CreateObject<Node> ();
Ptr<Node> n1 = CreateObject<Node> ();
NodeContainer net (n0, n1);
CsmaHelper csma;
NetDeviceContainer ndc = csma.Install (net);

NS_LOG_INFO ("Assign IPv6 Addresses.");
Ipv6AddressHelper ipv6;
ipv6.SetBase (Ipv6Address ("2001:db8::"), Ipv6Prefix (64));
Ipv6InterfaceContainer ic = ipv6.Assign (ndc);

This method will add two global IPv6 addresses to the nodes. Note that, as usual for IPv6, all the nodes will also have a link-local address. Typically the first address on an interface will be the link-local one, with the global address(es) being the following ones.

Note that the global addesses will be derived from the MAC address. As a consequence, expect to have addresses similar to 2001:db8::200:ff:fe00:1.

It is possible to repeat the above to assign more than one global address to a node. However, due to the Ipv6AddressHelper singleton nature, one should first assign all the adddresses of a network, then change the network base (SetBase), then do a new assignment.

Alternatively, it is possible to assign a specific address to a node:

Ptr<Node> n0 = CreateObject<Node> ();
NodeContainer net (n0);
CsmaHelper csma;
NetDeviceContainer ndc = csma.Install (net);

NS_LOG_INFO ("Specifically Assign an IPv6 Address.");
Ipv6AddressHelper ipv6;
Ptr<NetDevice> device = ndc.Get (0);
Ptr<Node> node = device->GetNode ();
Ptr<Ipv6> ipv6proto = node->GetObject<Ipv6> ();
int32_t ifIndex = 0;
ifIndex = ipv6proto->GetInterfaceForDevice (device);
Ipv6InterfaceAddress ipv6Addr = Ipv6InterfaceAddress (Ipv6Address ("2001:db8:f00d:cafe::42"), Ipv6Prefix (64));
ipv6proto->AddAddress (ifIndex, ipv6Addr);
Auto-generated IPv6 adddresses

This is accomplished by relying on the RADVD protocol, implemented by the class Radvd. At the time there is no helper for this application, and the use is rather difficult (see examples/ipv6/radvd.cc).

Upon using this method, the nodes will acquire dynamically (i.e., during the simulation) one (or more) global address(es) according to the RADVD configuration. These addresses will be bases on the RADVD announced prefix and the node’s EUI-64.

Random-generated IPv6 adddresses

While IPv6 real nodes will use randomly generated addresses to protect privacy, ns-3 does NOT have this capability. This feature haven’t been so far considered as interesting for simulation.

Duplicate Address Detection (DAD)

Nodes will perform DAD (it can be disabled using an Icmpv6L4Protocol attribute). Upon receiving a DAD, however, nodes will not react to it. As is: DAD reaction is incomplete so far. The main reason relies on the missing random-generated address capability. Moreover, since ns-3 nodes will usually be well-behaving, therea should’t be any Duplicate Address. This might be changed in the future, so as to avoid issues with real-world integrated simulations.

Host and Router behaviour in IPv6 and ns-3

In IPv6 there is a clear distinction between routers and hosts. As one might expect, routers can forward packets from an interface to another interface, while hosts drop packets not directed to them.

Unfortunately, forwarding is not the only thing affected by this distinction, and forwarding itself might be fine-tuned, e.g., to forward packets incoming from an interface and drop packets from another interface.

In ns-3 a node is configured to be an host by default. There are two main ways to change this behaviour:

  • Using ns3::Ipv6InterfaceContainer SetForwarding(uint32_t i, bool router) where i is the interface index in the container.
  • Changing the ns3::Ipv6 attribute IpForward.

Either one can be used during the simulation.

A fine-grained setup can be accomplished by using ns3::Ipv6Interface SetForwarding (bool forward); which allows to change the behaviour on a per-interface-basis.

Note that the node-wide configuration only serves as a convenient method to enable/disable the ns3::Ipv6Interface specific setting. An Ipv6Interface added to a node with forwarding enabled will be set to be forwarding as well. This is really important when a node has interfaces added during the simulation.

According to the ns3::Ipv6Interface forwarding state, the following happens:

  • Forwarding OFF
  • The node will NOT reply to Router Solicitation
  • The node will react to Router Advertisement
  • The node will periodically send Router Solicitation
  • Routing protocols MUST DROP packets not directed to the node
  • Forwarding ON
  • The node will reply to Router Solicitation
  • The node will NOT react to Router Advertisement
  • The node will NOT send Router Solicitation
  • Routing protocols MUST forward packets

The behaviour is matching ip-sysctl.txt (http://www.kernel.org/doc/Documentation/networking/ip-sysctl.txt) with the difference that it’s not possible to override the behaviour using esoteric settings (e.g., forwarding but accept router advertisements, accept_ra=2, or forwarding but send router solicitations forwarding=2).

Consider carefully the implications of packet forwarding. As an example, a node will NOT send ICMPv6 PACKET_TOO_BIG messages from an interface with frowarding off. This is completely normal, as the Routing protocol will drop the packet before attempting to forward it.

Helpers

Typically the helpers used in IPv6 setup are:

  • ns3::InternetStackHelper
  • ns3::Ipv6AddressHelper
  • ns3::Ipv6InterfaceContainer

The use is almost identical to the corresponding IPv4 case, e.g.:

NodeContainer n;
n.Create (4);

NS_LOG_INFO ("Create IPv6 Internet Stack");
InternetStackHelper internetv6;
internetv6.Install (n);

NS_LOG_INFO ("Create channels.");
CsmaHelper csma;
NetDeviceContainer d = csma.Install (n);

NS_LOG_INFO ("Create networks and assign IPv6 Addresses.");
Ipv6AddressHelper ipv6;
ipv6.SetBase (Ipv6Address ("2001:db8::"), Ipv6Prefix (64));
Ipv6InterfaceContainer iic = ipv6.Assign (d);

Additionally, a common task is to enable forwarding on one of the nodes and to setup a default route toward it in the other nodes, e.g.:

iic.SetForwarding (0, true);
iic.SetDefaultRouteInAllNodes (0);

This will enable forwarding on the node 0 and will setup a default route in ns3::Ipv6StaticRouting on all the other nodes. Note that this requires that Ipv6StaticRouting is present in the nodes.

The IPv6 routing helpers enable the user to perform specific tasks on the particular routing algorith and to print the routing tables.

Attributes

Many classes in the ns-3 IPv6 implementation contain attributes. The most useful ones are:

  • ns3::Ipv6
  • IpForward, boolean, default false. Globally enable or disable IP forwarding for all current and future IPv6 devices.
  • MtuDiscover, boolean, default true. If disabled, every interface will have its MTU set to 1280 bytes.
  • ns3::Ipv6L3Protocol
  • DefaultTtl, uint8_t, default 64. The TTL value set by default on all outgoing packets generated on this node.
  • SendIcmpv6Redirect, boolean, default true. Send the ICMPv6 Redirect when appropriate.
  • ns3::Icmpv6L4Protocol
  • DAD, boolean, default true. Always do DAD (Duplicate Address Detection) check.
  • ns3::NdiscCache
  • UnresolvedQueueSize, uint32_t, default 3. Size of the queue for packets pending an NA reply.
Output

The IPv6 stack provides some useful trace sources:

  • ns3::Ipv6L3Protocol
  • Tx, Send IPv6 packet to outgoing interface.
  • Rx, Receive IPv6 packet from incoming interface.
  • Drop, Drop IPv6 packet.
  • ns3::Ipv6Extension
  • Drop, Drop IPv6 packet.

The latest trace source is generated when a packet contains an unknown option blocking its processing.

Mind that ns3::NdiscCache could drop packets as well, and they are not logged in a trace source (yet). This might generate some confusion in the sent/received packets counters.

Advanced Usage
IPv6 maximum transmission unit (MTU) and fragmentation

ns-3 NetDevices define the MTU according to the L2 simulated Device. IPv6 requires that the minimum MTU is 1280 bytes, so all NetDevices are required to support at least this MTU. This is the link-MTU.

In order to support different MTUs in a source-destination path, ns-3 IPv6 model can perform fragmentation. This can be either triggered by receiving a packet bigger than the link-MTU from the L4 protocols (UDP, TCP, etc.), or by receving an ICMPv6 PACKET_TOO_BIG message. The model mimics RFC 1981, with the following notable exceptions:

  • L4 protocols are not informed of the Path MTU change
  • TCP can not change its Segment Size according to the Path-MTU.

Both limitations are going to be removed in due time.

The Path-MTU cache is currently based on the source-destination IPv6 addresses. Further classifications (e.g., flow label) are possible but not yet implemented.

The Path-MTU default validity time is 10 minutes. After the cache entry expiration, the Path-MTU information is removed and the next packet will (eventually) trigger a new ICMPv6 PACKET_TOO_BIG message. Note that 1) this is consistent with the RFC specification and 2) L4 protocols are responsible for retransmitting the packets.

Examples

The examples for IPv6 are in the directory examples/ipv6. These examples focus on the most interesting IPv6 peculiarities, such as fragmentation, redirect and so on.

Moreover, most TCP and UDP examples located in examples/udp, examples/tcp, etc. have a command-line option to use IPv6 instead of IPv4.

Troubleshooting

There are just a few pitfalls to avoid while using ns-3 IPv6.

Routing loops

Since the only (so far) routing scheme available for IPv6 is ns3::Ipv6StaticRouting, default router have to be setup manually. When there are two or more routers in a network (e.g., node A and node B), avoid using the helper function SetDefaultRouteInAllNodes for more than one router.

The consequence would be to install a default route to B in A and a default route pointing to A in B, generating a loop.

Global address leakage

Remember that addresses in IPv6 are global by definition. When using IPv6 with an emulation ns-3 capability, avoid at all costs address leakage toward the global Internet. It is advisable to setup an external firewall to prevent leakage.

2001:DB8::/32 addresses

IPv6 standard (RFC 3849) defines the 2001:DB8::/32 address class for the documentation. This manual uses this convention. The addresses in this class are, however, only usable in a document, and routers should discard them.

Validation

The IPv6 protocols has not yet been extensively validated against real implementations. The actual tests involve mainly performing checks of the .pcap trace files with Wireshark, and the results are positive.

Routing overview

ns-3 is intended to support traditional routing approaches and protocols, support ports of open source routing implementations, and facilitate research into unorthodox routing techniques. The overall routing architecture is described below in Routing architecture. Users who wish to just read about how to configure global routing for wired topologies can read Global centralized routing. Unicast routing protocols are described in Unicast routing. Multicast routing is documented in Multicast routing.

Routing architecture

_images/routing.png

Overview of routing

Overview of routing shows the overall routing architecture for Ipv4. The key objects are Ipv4L3Protocol, Ipv4RoutingProtocol(s) (a class to which all routing/forwarding has been delegated from Ipv4L3Protocol), and Ipv4Route(s).

Ipv4L3Protocol must have at least one Ipv4RoutingProtocol added to it at simulation setup time. This is done explicitly by calling Ipv4::SetRoutingProtocol ().

The abstract base class Ipv4RoutingProtocol () declares a minimal interface, consisting of two methods: RouteOutput () and RouteInput (). For packets traveling outbound from a host, the transport protocol will query Ipv4 for the Ipv4RoutingProtocol object interface, and will request a route via Ipv4RoutingProtocol::RouteOutput (). A Ptr to Ipv4Route object is returned. This is analagous to a dst_cache entry in Linux. The Ipv4Route is carried down to the Ipv4L3Protocol to avoid a second lookup there. However, some cases (e.g. Ipv4 raw sockets) will require a call to RouteOutput() directly from Ipv4L3Protocol.

For packets received inbound for forwarding or delivery, the following steps occur. Ipv4L3Protocol::Receive() calls Ipv4RoutingProtocol::RouteInput(). This passes the packet ownership to the Ipv4RoutingProtocol object. There are four callbacks associated with this call:

  • LocalDeliver
  • UnicastForward
  • MulticastForward
  • Error

The Ipv4RoutingProtocol must eventually call one of these callbacks for each packet that it takes responsibility for. This is basically how the input routing process works in Linux.

_images/routing-specialization.png

Ipv4Routing specialization.

This overall architecture is designed to support different routing approaches, including (in the future) a Linux-like policy-based routing implementation, proactive and on-demand routing protocols, and simple routing protocols for when the simulation user does not really care about routing.

Ipv4Routing specialization. illustrates how multiple routing protocols derive from this base class. A class Ipv4ListRouting (implementation class Ipv4ListRoutingImpl) provides the existing list routing approach in ns-3. Its API is the same as base class Ipv4Routing except for the ability to add multiple prioritized routing protocols (Ipv4ListRouting::AddRoutingProtocol(), Ipv4ListRouting::GetRoutingProtocol()).

The details of these routing protocols are described below in Unicast routing. For now, we will first start with a basic unicast routing capability that is intended to globally build routing tables at simulation time t=0 for simulation users who do not care about dynamic routing.

Global centralized routing

Global centralized routing is sometimes called “God” routing; it is a special implementation that walks the simulation topology and runs a shortest path algorithm, and populates each node’s routing tables. No actual protocol overhead (on the simulated links) is incurred with this approach. It does have a few constraints:

  • Wired only: It is not intended for use in wireless networks.
  • Unicast only: It does not do multicast.
  • Scalability: Some users of this on large topologies (e.g. 1000 nodes) have noticed that the current implementation is not very scalable. The global centralized routing will be modified in the future to reduce computations and runtime performance.

Presently, global centralized IPv4 unicast routing over both point-to-point and shared (CSMA) links is supported.

By default, when using the ns-3 helper API and the default InternetStackHelper, global routing capability will be added to the node, and global routing will be inserted as a routing protocol with lower priority than the static routes (i.e., users can insert routes via Ipv4StaticRouting API and they will take precedence over routes found by global routing).

Global Unicast Routing API

The public API is very minimal. User scripts include the following:

#include "ns3/internet-module.h"

If the default InternetStackHelper is used, then an instance of global routing will be aggregated to each node. After IP addresses are configured, the following function call will cause all of the nodes that have an Ipv4 interface to receive forwarding tables entered automatically by the GlobalRouteManager:

Ipv4GlobalRoutingHelper::PopulateRoutingTables ();

Note: A reminder that the wifi NetDevice will work but does not take any wireless effects into account. For wireless, we recommend OLSR dynamic routing described below.

It is possible to call this function again in the midst of a simulation using the following additional public function:

Ipv4GlobalRoutingHelper::RecomputeRoutingTables ();

which flushes the old tables, queries the nodes for new interface information, and rebuilds the routes.

For instance, this scheduling call will cause the tables to be rebuilt at time 5 seconds:

Simulator::Schedule (Seconds (5),
                     &Ipv4GlobalRoutingHelper::RecomputeRoutingTables);

There are two attributes that govern the behavior. The first is Ipv4GlobalRouting::RandomEcmpRouting. If set to true, packets are randomly routed across equal-cost multipath routes. If set to false (default), only one route is consistently used. The second is Ipv4GlobalRouting::RespondToInterfaceEvents. If set to true, dynamically recompute the global routes upon Interface notification events (up/down, or add/remove address). If set to false (default), routing may break unless the user manually calls RecomputeRoutingTables() after such events. The default is set to false to preserve legacy ns-3 program behavior.

Global Routing Implementation

This section is for those readers who care about how this is implemented. A singleton object (GlobalRouteManager) is responsible for populating the static routes on each node, using the public Ipv4 API of that node. It queries each node in the topology for a “globalRouter” interface. If found, it uses the API of that interface to obtain a “link state advertisement (LSA)” for the router. Link State Advertisements are used in OSPF routing, and we follow their formatting.

It is important to note that all of these computations are done before packets are flowing in the network. In particular, there are no overhead or control packets being exchanged when using this implementation. Instead, this global route manager just walks the list of nodes to build the necessary information and configure each node’s routing table.

The GlobalRouteManager populates a link state database with LSAs gathered from the entire topology. Then, for each router in the topology, the GlobalRouteManager executes the OSPF shortest path first (SPF) computation on the database, and populates the routing tables on each node.

The quagga (http://www.quagga.net) OSPF implementation was used as the basis for the routing computation logic. One benefit of following an existing OSPF SPF implementation is that OSPF already has defined link state advertisements for all common types of network links:

  • point-to-point (serial links)
  • point-to-multipoint (Frame Relay, ad hoc wireless)
  • non-broadcast multiple access (ATM)
  • broadcast (Ethernet)

Therefore, we think that enabling these other link types will be more straightforward now that the underlying OSPF SPF framework is in place.

Presently, we can handle IPv4 point-to-point, numbered links, as well as shared broadcast (CSMA) links. Equal-cost multipath is also supported. Although wireless link types are supported by the implementation, note that due to the nature of this implementation, any channel effects will not be considered and the routing tables will assume that every node on the same shared channel is reachable from every other node (i.e. it will be treated like a broadcast CSMA link).

The GlobalRouteManager first walks the list of nodes and aggregates a GlobalRouter interface to each one as follows:

typedef std::vector < Ptr<Node> >::iterator Iterator;
for (Iterator i = NodeList::Begin (); i != NodeList::End (); i++)
  {
    Ptr<Node> node = *i;
    Ptr<GlobalRouter> globalRouter = CreateObject<GlobalRouter> (node);
    node->AggregateObject (globalRouter);
  }

This interface is later queried and used to generate a Link State Advertisement for each router, and this link state database is fed into the OSPF shortest path computation logic. The Ipv4 API is finally used to populate the routes themselves.

Unicast routing

There are presently seven unicast routing protocols defined for IPv4 and three for IPv6:

  • class Ipv4StaticRouting (covering both unicast and multicast)
  • IPv4 Optimized Link State Routing (OLSR) (a MANET protocol defined in RFC 3626)
  • IPv4 Ad Hoc On Demand Distance Vector (AODV) (a MANET protocol defined in RFC 3561)
  • IPv4 Destination Sequenced Distance Vector (DSDV) (a MANET protocol)
  • IPv4 Dynamic Source Routing (DSR) (a MANET protocol)
  • class Ipv4ListRouting (used to store a prioritized list of routing protocols)
  • class Ipv4GlobalRouting (used to store routes computed by the global route manager, if that is used)
  • class Ipv4NixVectorRouting (a more efficient version of global routing that stores source routes in a packet header field)
  • class Ipv6ListRouting (used to store a prioritized list of routing protocols)
  • class Ipv6StaticRouting
  • class RipNg - the IPv6 RIPng protocol (RFC 2080)

In the future, this architecture should also allow someone to implement a Linux-like implementation with routing cache, or a Click modular router, but those are out of scope for now.

Ipv[4,6]ListRouting

This section describes the current default ns-3 Ipv[4,6]RoutingProtocol. Typically, multiple routing protocols are supported in user space and coordinate to write a single forwarding table in the kernel. Presently in ns-3, the implementation instead allows for multiple routing protocols to build/keep their own routing state, and the IP implementation will query each one of these routing protocols (in some order determined by the simulation author) until a route is found.

We chose this approach because it may better facilitate the integration of disparate routing approaches that may be difficult to coordinate the writing to a single table, approaches where more information than destination IP address (e.g., source routing) is used to determine the next hop, and on-demand routing approaches where packets must be cached.

Ipv[4,6]4ListRouting::AddRoutingProtocol

Classes Ipv4ListRouting and Ipv6ListRouting provides a pure virtual function declaration for the method that allows one to add a routing protocol:

void AddRoutingProtocol (Ptr<Ipv4RoutingProtocol> routingProtocol,
                         int16_t priority);

void AddRoutingProtocol (Ptr<Ipv6RoutingProtocol> routingProtocol,
                         int16_t priority);

These methods are implemented respectively by class Ipv4ListRoutingImpl and by class Ipv6ListRoutingImpl in the internet module.

The priority variable above governs the priority in which the routing protocols are inserted. Notice that it is a signed int. By default in ns-3, the helper classes will instantiate a Ipv[4,6]ListRoutingImpl object, and add to it an Ipv[4,6]StaticRoutingImpl object at priority zero. Internally, a list of Ipv[4,6]RoutingProtocols is stored, and and the routing protocols are each consulted in decreasing order of priority to see whether a match is found. Therefore, if you want your Ipv4RoutingProtocol to have priority lower than the static routing, insert it with priority less than 0; e.g.:

Ptr<MyRoutingProtocol> myRoutingProto = CreateObject<MyRoutingProtocol> ();
listRoutingPtr->AddRoutingProtocol (myRoutingProto, -10);

Upon calls to RouteOutput() or RouteInput(), the list routing object will search the list of routing protocols, in priority order, until a route is found. Such routing protocol will invoke the appropriate callback and no further routing protocols will be searched.

RIPng

This IPv6 routing protocol (RFC 2080) is the evolution of the well-known RIPv1 anf RIPv2 (see RFC 1058 and RFC 1723) routing protocols for IPv4.

The protocol is very simple, and it is normally suitable for flat, simple network topologies.

RIPng is strongly based on RIPv1 and RIPv2, and it have the very same goals and limitations. In particular, RIP considers any route with a metric equal or greater than 16 as unreachable. As a consequence, the maximum number of hops is the network must be less than 15 (the number of routers is not set). Users are encouraged to read RFC 2080 and RFC 1058 to fully understand RIPng behaviour and limitations.

Routing convergence

RIPng uses a Distance-Vector algorithm, and routes are updated according to the Bellman-Ford algorithm (sometimes known as Ford-Fulkerson algorithm). The algorithm has a convergence time of O(|V|*|E|) where |V| and |E| are the number of vertices (routers) and edges (links) respectively. It should be stressed that the convergence time is the number of steps in the algorithm, and each step is triggered by a message. Since Triggered Updates (i.e., when a route is changed) have a 1-5 seconds cooldown, the toplogy can require some time to be stabilized.

Users should be aware that, during routing tables construction, the routers might drop packets. Data traffic should be sent only after a time long enough to allow RIPng to build the network topology. Usually 80 seconds should be enough to have a suboptimal (but working) routing setup. This includes the time needed to propagate the routes to the most distant router (16 hops) with Triggered Updates.

If the network topology is changed (e.g., a link is broken), the recovery time might be quite high, and it might be even higher than the initial setup time. Moreover, the network topology recovery is affected by the Split Horizoning strategy.

The example examples/routing/ripng-simple-network.cc shows both the network setup and network recovery phases.

Split Horizoning

Split Horizon is a strategy to prevent routing instability. Three options are possible:

  • No Split Horizon
  • Split Horizon
  • Poison Reverse

In the first case, routes are advertised on all the router’s interfaces. In the second case, routers will not advertise a route on the interface from which it was learned. Poison Reverse will advertise the route on the interface from which it was learned, but with a metric of 16 (infinity). For a full analysis of the three techniques, see RFC 1058, section 2.2.

The example ripng-simple-network.cc is based on the network toplogy described in the RFC, but it does not show the effect described there.

The reason are the Triggered Updates, together with the fact that when a router invalidates a route, it will immediately propagate the route unreachability, thus preventing most of the issues described in the RFC.

However, with complex toplogies, it is still possible to have route instability phenomena similar to the one described in the RFC after a link failure. As a consequence, all the considerations about Split Horizon remanins valid.

Default routes

RIPng protocol should be installed only on routers. As a consequence, nodes will not know what is the default router.

To overcome this limitation, users should either install the default route manually (e.g., by resorting to Ipv6StaticRouting), or by using RADVd. RADVd is available in ns-3 in the Applications module, and it is strongly suggested.

Protocol parameters and options

The RIPng ns-3 implementation allows to change all the timers associated with route updates and routes lifetime.

Moreover, users can change the interface metrics on a per-node basis.

The type of Split Horizoning (to avoid routes back-propagation) can be selected on a per-node basis, with the choices being “no split horizon”, “split horizon” and “poison reverse”. See RFC 2080 for further details, and RFC 1058 for a complete discussion on the split horizoning strategies.

Limitations

There is no support for the Next Hop option (RFC 2080, Section 2.1.1). The Next Hop option is useful when RIPng is not being run on all of the routers on a network. Support for this option may be considered in the future.

There is no support for CIDR prefix aggregation. As a result, both routing tables and route advertisements may be larger than necessary. Prefix aggregation may be added in the future.

Multicast routing

The following function is used to add a static multicast route to a node:

void
Ipv4StaticRouting::AddMulticastRoute (Ipv4Address origin,
                                      Ipv4Address group,
                                      uint32_t inputInterface,
                                      std::vector<uint32_t> outputInterfaces);

A multicast route must specify an origin IP address, a multicast group and an input network interface index as conditions and provide a vector of output network interface indices over which packets matching the conditions are sent.

Typically there are two main types of multicast routes: routes of the first kind are used during forwarding. All of the conditions must be explicitly provided. The second kind of routes are used to get packets off of a local node. The difference is in the input interface. Routes for forwarding will always have an explicit input interface specified. Routes off of a node will always set the input interface to a wildcard specified by the index Ipv4RoutingProtocol::IF_INDEX_ANY.

For routes off of a local node wildcards may be used in the origin and multicast group addresses. The wildcard used for Ipv4Adresses is that address returned by Ipv4Address::GetAny () – typically “0.0.0.0”. Usage of a wildcard allows one to specify default behavior to varying degrees.

For example, making the origin address a wildcard, but leaving the multicast group specific allows one (in the case of a node with multiple interfaces) to create different routes using different output interfaces for each multicast group.

If the origin and multicast addresses are made wildcards, you have created essentially a default multicast address that can forward to multiple interfaces. Compare this to the actual default multicast address that is limited to specifying a single output interface for compatibility with existing functionality in other systems.

Another command sets the default multicast route:

void
Ipv4StaticRouting::SetDefaultMulticastRoute (uint32_t outputInterface);

This is the multicast equivalent of the unicast version SetDefaultRoute. We tell the routing system what to do in the case where a specific route to a destination multicast group is not found. The system forwards packets out the specified interface in the hope that “something out there” knows better how to route the packet. This method is only used in initially sending packets off of a host. The default multicast route is not consulted during forwarding – exact routes must be specified using AddMulticastRoute for that case.

Since we’re basically sending packets to some entity we think may know better what to do, we don’t pay attention to “subtleties” like origin address, nor do we worry about forwarding out multiple interfaces. If the default multicast route is set, it is returned as the selected route from LookupStatic irrespective of origin or multicast group if another specific route is not found.

Finally, a number of additional functions are provided to fetch and remove multicast routes:

uint32_t GetNMulticastRoutes (void) const;

Ipv4MulticastRoute *GetMulticastRoute (uint32_t i) const;

Ipv4MulticastRoute *GetDefaultMulticastRoute (void) const;

bool RemoveMulticastRoute (Ipv4Address origin,
                           Ipv4Address group,
                           uint32_t inputInterface);

void RemoveMulticastRoute (uint32_t index);

TCP models in ns-3

This chapter describes the TCP models available in ns-3.

Generic support for TCP

ns-3 was written to support multiple TCP implementations. The implementations inherit from a few common header classes in the src/network directory, so that user code can swap out implementations with minimal changes to the scripts.

There are two important abstract base classes:

  • class TcpSocket: This is defined in src/internet/model/tcp-socket.{cc,h}. This class exists for hosting TcpSocket attributes that can be reused across different implementations. For instance, the attribute InitialCwnd can be used for any of the implementations that derive from class TcpSocket.
  • class TcpSocketFactory: This is used by the layer-4 protocol instance to create TCP sockets of the right type.

There are presently three implementations of TCP available for ns-3.

It should also be mentioned that various ways of combining virtual machines with ns-3 makes available also some additional TCP implementations, but those are out of scope for this chapter.

ns-3 TCP

Until ns-3.10 release, ns-3 contained a port of the TCP model from GTNetS. This implementation was substantially rewritten by Adriam Tam for ns-3.10. The model is a full TCP, in that it is bidirectional and attempts to model the connection setup and close logic.

The implementation of TCP is contained in the following files:

src/internet/model/tcp-header.{cc,h}
src/internet/model/tcp-l4-protocol.{cc,h}
src/internet/model/tcp-socket-factory-impl.{cc,h}
src/internet/model/tcp-socket-base.{cc,h}
src/internet/model/tcp-tx-buffer.{cc,h}
src/internet/model/tcp-rx-buffer.{cc,h}
src/internet/model/tcp-rfc793.{cc,h}
src/internet/model/tcp-tahoe.{cc,h}
src/internet/model/tcp-reno.{cc,h}
src/internet/model/tcp-westwood.{cc,h}
src/internet/model/tcp-newreno.{cc,h}
src/internet/model/rtt-estimator.{cc,h}
src/network/model/sequence-number.{cc,h}

Different variants of TCP congestion control are supported by subclassing the common base class TcpSocketBase. Several variants are supported, including RFC 793 (no congestion control), Tahoe, Reno, Westwood, Westwood+, and NewReno. NewReno is used by default. See the Usage section of this document for on how to change the default TCP variant used in simulation.

Usage

In many cases, usage of TCP is set at the application layer by telling the ns-3 application which kind of socket factory to use.

Using the helper functions defined in src/applications/helper and src/network/helper, here is how one would create a TCP receiver:

// Create a packet sink on the star "hub" to receive these packets
uint16_t port = 50000;
Address sinkLocalAddress(InetSocketAddress (Ipv4Address::GetAny (), port));
PacketSinkHelper sinkHelper ("ns3::TcpSocketFactory", sinkLocalAddress);
ApplicationContainer sinkApp = sinkHelper.Install (serverNode);
sinkApp.Start (Seconds (1.0));
sinkApp.Stop (Seconds (10.0));

Similarly, the below snippet configures OnOffApplication traffic source to use TCP:

// Create the OnOff applications to send TCP to the server
OnOffHelper clientHelper ("ns3::TcpSocketFactory", Address ());

The careful reader will note above that we have specified the TypeId of an abstract base class TcpSocketFactory. How does the script tell ns-3 that it wants the native ns-3 TCP vs. some other one? Well, when internet stacks are added to the node, the default TCP implementation that is aggregated to the node is the ns-3 TCP. This can be overridden as we show below when using Network Simulation Cradle. So, by default, when using the ns-3 helper API, the TCP that is aggregated to nodes with an Internet stack is the native ns-3 TCP.

To configure behavior of TCP, a number of parameters are exported through the ns-3 attribute system. These are documented in the Doxygen <http://www.nsnam.org/doxygen/classns3_1_1_tcp_socket.html> for class TcpSocket. For example, the maximum segment size is a settable attribute.

To set the default socket type before any internet stack-related objects are created, one may put the following statement at the top of the simulation program:

Config::SetDefault ("ns3::TcpL4Protocol::SocketType", StringValue ("ns3::TcpTahoe"));

For users who wish to have a pointer to the actual socket (so that socket operations like Bind(), setting socket options, etc. can be done on a per-socket basis), Tcp sockets can be created by using the Socket::CreateSocket() method. The TypeId passed to CreateSocket() must be of type ns3::SocketFactory, so configuring the underlying socket type must be done by twiddling the attribute associated with the underlying TcpL4Protocol object. The easiest way to get at this would be through the attribute configuration system. In the below example, the Node container “n0n1” is accessed to get the zeroth element, and a socket is created on this node:

// Create and bind the socket...
TypeId tid = TypeId::LookupByName ("ns3::TcpTahoe");
Config::Set ("/NodeList/*/$ns3::TcpL4Protocol/SocketType", TypeIdValue (tid));
Ptr<Socket> localSocket =
  Socket::CreateSocket (n0n1.Get (0), TcpSocketFactory::GetTypeId ());

Above, the “*” wild card for node number is passed to the attribute configuration system, so that all future sockets on all nodes are set to Tahoe, not just on node ‘n0n1.Get (0)’. If one wants to limit it to just the specified node, one would have to do something like:

// Create and bind the socket...
TypeId tid = TypeId::LookupByName ("ns3::TcpTahoe");
std::stringstream nodeId;
nodeId << n0n1.Get (0)->GetId ();
std::string specificNode = "/NodeList/" + nodeId.str () + "/$ns3::TcpL4Protocol/SocketType";
Config::Set (specificNode, TypeIdValue (tid));
Ptr<Socket> localSocket =
  Socket::CreateSocket (n0n1.Get (0), TcpSocketFactory::GetTypeId ());

Once a TCP socket is created, one will want to follow conventional socket logic and either connect() and send() (for a TCP client) or bind(), listen(), and accept() (for a TCP server). See Sockets APIs for a review of how sockets are used in ns-3.

Validation

Several TCP validation test results can be found in the wiki page describing this implementation.

Current limitations
  • SACK is not supported

Network Simulation Cradle

The Network Simulation Cradle (NSC) is a framework for wrapping real-world network code into simulators, allowing simulation of real-world behavior at little extra cost. This work has been validated by comparing situations using a test network with the same situations in the simulator. To date, it has been shown that the NSC is able to produce extremely accurate results. NSC supports four real world stacks: FreeBSD, OpenBSD, lwIP and Linux. Emphasis has been placed on not changing any of the network stacks by hand. Not a single line of code has been changed in the network protocol implementations of any of the above four stacks. However, a custom C parser was built to programmatically change source code.

NSC has previously been ported to ns-2 and OMNeT++, and was was added to ns-3 in September 2008 (ns-3.2 release). This section describes the ns-3 port of NSC and how to use it.

To some extent, NSC has been superseded by the Linux kernel support within Direct Code Execution (DCE). However, NSC is still available through the bake build system. NSC supports Linux kernels 2.6.18 and 2.6.26, but newer versions of the kernel have not been ported.

Prerequisites

Presently, NSC has been tested and shown to work on these platforms: Linux i386 and Linux x86-64. NSC does not support powerpc. Use on FreeBSD or OS X is unsupported (although it may be able to work).

Building NSC requires the packages flex and bison.

Configuring and Downloading

As of ns-3.17 or later, NSC must either be downloaded separately from its own repository, or downloading when using the bake build system of ns-3.

For ns-3.17 or later releases, when using bake, one must configure NSC as part of an “allinone” configuration, such as:

$ cd bake
$ python bake.py configure -e ns-allinone-3.19
$ python bake.py download
$ python bake.py build

Instead of a released version, one may use the ns-3 development version by specifying “ns-3-allinone” to the configure step above.

NSC may also be downloaded from its download site using Mercurial:

$ hg clone https://secure.wand.net.nz/mercurial/nsc

Prior to the ns-3.17 release, NSC was included in the allinone tarball and the released version did not need to be separately downloaded.

Building and validating

NSC may be built as part of the bake build process; alternatively, one may build NSC by itself using its build system; e.g.:

$ cd nsc-dev
$ python scons.py

Once NSC has been built either manually or through the bake system, change into the ns-3 source directory and try running the following configuration:

$ ./waf configure

If NSC has been previously built and found by waf, then you will see:

Network Simulation Cradle     : enabled

If NSC has not been found, you will see:

Network Simulation Cradle     : not enabled (NSC not found (see option --with-nsc))

In this case, you must pass the relative or absolute path to the NSC libraries with the “–with-nsc” configure option; e.g.

$ ./waf configure --with-nsc=/path/to/my/nsc/directory

For ns-3 releases prior to the ns-3.17 release, using the build.py script in ns-3-allinone directory, NSC will be built by default unless the platform does not support it. To explicitly disable it when building ns-3, type:

$ ./waf configure --enable-examples --enable-tests --disable-nsc

If waf detects NSC, then building ns-3 with NSC is performed the same way with waf as without it. Once ns-3 is built, try running the following test suite:

$ ./test.py -s ns3-tcp-interoperability

If NSC has been successfully built, the following test should show up in the results:

PASS TestSuite ns3-tcp-interoperability

This confirms that NSC is ready to use.

Usage

There are a few example files. Try:

$ ./waf --run tcp-nsc-zoo
$ ./waf --run tcp-nsc-lfn

These examples will deposit some .pcap files in your directory, which can be examined by tcpdump or wireshark.

Let’s look at the examples/tcp/tcp-nsc-zoo.cc file for some typical usage. How does it differ from using native ns-3 TCP? There is one main configuration line, when using NSC and the ns-3 helper API, that needs to be set:

InternetStackHelper internetStack;

internetStack.SetNscStack ("liblinux2.6.26.so");
// this switches nodes 0 and 1 to NSCs Linux 2.6.26 stack.
internetStack.Install (n.Get(0));
internetStack.Install (n.Get(1));

The key line is the SetNscStack. This tells the InternetStack helper to aggregate instances of NSC TCP instead of native ns-3 TCP to the remaining nodes. It is important that this function be called before calling the Install() function, as shown above.

Which stacks are available to use? Presently, the focus has been on Linux 2.6.18 and Linux 2.6.26 stacks for ns-3. To see which stacks were built, one can execute the following find command at the ns-3 top level directory:

$ find nsc -name "*.so" -type f
nsc/linux-2.6.18/liblinux2.6.18.so
nsc/linux-2.6.26/liblinux2.6.26.so

This tells us that we may either pass the library name liblinux2.6.18.so or liblinux2.6.26.so to the above configuration step.

Stack configuration

NSC TCP shares the same configuration attributes that are common across TCP sockets, as described above and documented in Doxygen

Additionally, NSC TCP exports a lot of configuration variables into the ns-3 attributes system, via a sysctl-like interface. In the examples/tcp/tcp-nsc-zoo example, you can see the following configuration:

// this disables TCP SACK, wscale and timestamps on node 1 (the attributes
  represent sysctl-values).
Config::Set ("/NodeList/1/$ns3::Ns3NscStack<linux2.6.26>/net.ipv4.tcp_sack",
  StringValue ("0"));
Config::Set ("/NodeList/1/$ns3::Ns3NscStack<linux2.6.26>/net.ipv4.tcp_timestamps",
StringValue ("0"));
Config::Set ("/NodeList/1/$ns3::Ns3NscStack<linux2.6.26>/net.ipv4.tcp_window_scaling",
StringValue ("0"));

These additional configuration variables are not available to native ns-3 TCP.

Also note that default values for TCP attributes in ns-3 TCP may differ from the nsc TCP implementation. Specifically in ns-3:

  1. TCP default MSS is 536
  2. TCP Delayed Ack count is 2

Therefore when making comparisons between results obtained using nsc and ns-3 TCP, care must be taken to ensure these values are set appropriately. See /examples/tcp/tcp-nsc-comparision.cc for an example.

NSC API

This subsection describes the API that NSC presents to ns-3 or any other simulator. NSC provides its API in the form of a number of classes that are defined in sim/sim_interface.h in the nsc directory.

  • INetStack INetStack contains the ‘low level’ operations for the operating system network stack, e.g. in and output functions from and to the network stack (think of this as the ‘network driver interface’. There are also functions to create new TCP or UDP sockets.
  • ISendCallback This is called by NSC when a packet should be sent out to the network. This simulator should use this callback to re-inject the packet into the simulator so the actual data can be delivered/routed to its destination, where it will eventually be handed into Receive() (and eventually back to the receivers NSC instance via INetStack->if_receive() ).
  • INetStreamSocket This is the structure defining a particular connection endpoint (file descriptor). It contains methods to operate on this endpoint, e.g. connect, disconnect, accept, listen, send_data/read_data, ...
  • IInterruptCallback This contains the wakeup callback, which is called by NSC whenever something of interest happens. Think of wakeup() as a replacement of the operating systems wakeup function: Whenever the operating system would wake up a process that has been waiting for an operation to complete (for example the TCP handshake during connect()), NSC invokes the wakeup() callback to allow the simulator to check for state changes in its connection endpoints.
ns-3 implementation

The ns-3 implementation makes use of the above NSC API, and is implemented as follows.

The three main parts are:

  • ns3::NscTcpL4Protocol: a subclass of Ipv4L4Protocol (and two nsc classes: ISendCallback and IInterruptCallback)
  • ns3::NscTcpSocketImpl: a subclass of TcpSocket
  • ns3::NscTcpSocketFactoryImpl: a factory to create new NSC sockets

src/internet/model/nsc-tcp-l4-protocol is the main class. Upon Initialization, it loads an nsc network stack to use (via dlopen()). Each instance of this class may use a different stack. The stack (=shared library) to use is set using the SetNscLibrary() method (at this time its called indirectly via the internet stack helper). The nsc stack is then set up accordingly (timers etc). The NscTcpL4Protocol::Receive() function hands the packet it receives (must be a complete tcp/ip packet) to the nsc stack for further processing. To be able to send packets, this class implements the nsc send_callback method. This method is called by nsc whenever the nsc stack wishes to send a packet out to the network. Its arguments are a raw buffer, containing a complete TCP/IP packet, and a length value. This method therefore has to convert the raw data to a Ptr<Packet> usable by ns-3. In order to avoid various ipv4 header issues, the nsc ip header is not included. Instead, the tcp header and the actual payload are put into the Ptr<Packet>, after this the Packet is passed down to layer 3 for sending the packet out (no further special treatment is needed in the send code path).

This class calls ns3::NscTcpSocketImpl both from the nsc wakeup() callback and from the Receive path (to ensure that possibly queued data is scheduled for sending).

src/internet/model/nsc-tcp-socket-impl implements the nsc socket interface. Each instance has its own nscTcpSocket. Data that is Send() will be handed to the nsc stack via m_nscTcpSocket->send_data(). (and not to nsc-tcp-l4, this is the major difference compared to ns-3 TCP). The class also queues up data that is Send() before the underlying descriptor has entered an ESTABLISHED state. This class is called from the nsc-tcp-l4 class, when the nsc-tcp-l4 wakeup() callback is invoked by nsc. nsc-tcp-socket-impl then checks the current connection state (SYN_SENT, ESTABLISHED, LISTEN...) and schedules appropriate callbacks as needed, e.g. a LISTEN socket will schedule Accept to see if a new connection must be accepted, an ESTABLISHED socket schedules any pending data for writing, schedule a read callback, etc.

Note that ns3::NscTcpSocketImpl does not interact with nsc-tcp directly: instead, data is redirected to nsc. nsc-tcp calls the nsc-tcp-sockets of a node when its wakeup callback is invoked by nsc.

Limitations
  • NSC only works on single-interface nodes; attempting to run it on a multi-interface node will cause a program error.
  • Cygwin and OS X PPC are not supported; OS X Intel is not supported but may work
  • The non-Linux stacks of NSC are not supported in ns-3
  • Not all socket API callbacks are supported

For more information, see this wiki page.

PageBreak

Low-Rate Wireless Personal Area Network (LR-WPAN)

This chapter describes the implementation of ns-3 models for the low-rate, wireless personal area network (LR-WPAN) as specified by IEEE standard 802.15.4 (2006).

Model Description

The source code for the lr-wpan module lives in the directory src/lr-wpan.

Design

The model design closely follows the standard from an architectural standpoint.

_images/lr-wpan-arch.png

Architecture and scope of lr-wpan models

The grey areas in the figure (adapted from Fig 3. of IEEE Std. 802.15.4-2006) show the scope of the model.

The Spectrum NetDevice from Nicola Baldo is the basis for the implementation.

The implementation also plans to borrow from the ns-2 models developed by Zheng and Lee in the future.

APIs

The APIs closely follow the standard, adapted for ns-3 naming conventions and idioms. The APIs are organized around the concept of service primitives as shown in the following figure adapted from Figure 14 of IEEE Std. 802.15.4-2006.

_images/lr-wpan-primitives.png

Service primitives

The APIs are organized around four conceptual services and service access points (SAP):

  • MAC data service (MCPS)
  • MAC management service (MLME)
  • PHY data service (PD)
  • PHY management service (PLME)

In general, primitives are standardized as follows (e.g. Sec 7.1.1.1.1 of IEEE 802.15.4-2006)::

MCPS-DATA.request      (
                        SrcAddrMode,
                        DstAddrMode,
                        DstPANId,
                        DstAddr,
                        msduLength,
                        msdu,
                        msduHandle,
                        TxOptions,
                        SecurityLevel,
                        KeyIdMode,
                        KeySource,
                        KeyIndex
                        )

This maps to ns-3 classes and methods such as::

struct McpsDataRequestParameters
{
  uint8_t m_srcAddrMode;
  uint8_t m_dstAddrMode;
  ...
};

void
LrWpanMac::McpsDataRequest (McpsDataRequestParameters params)
{
...
}
MAC

The MAC at present implements the unslotted CSMA/CA variant, without beaconing. Currently there is no support for coordinators and the relavant APIs.

The implemented MAC is similar to Contiki’s NullMAC, i.e., a MAC without sleep features. The radio is assumed to be always active (receiving or transmitting), of completely shut down. Frame reception is not disabled while performing the CCA.

The main API supported is the data transfer API (McpsDataRequest/Indication/Confirm). CSMA/CA according to Stc 802.15.4-2006, section 7.5.1.4 is supported. Frame reception and rejection according to Std 802.15.4-2006, section 7.5.6.2 is supported, including acknowledgements. Only short addressing completely implemented. Various trace sources are supported, and trace sources can be hooked to sinks.

PHY

The physical layer components consist of a Phy model, an error rate model, and a loss model. The error rate model presently models the error rate for IEEE 802.15.4 2.4 GHz AWGN channel for OQPSK; the model description can be found in IEEE Std 802.15.4-2006, section E.4.1.7. The Phy model is based on SpectrumPhy and it follows specification described in section 6 of IEEE Std 802.15.4-2006. It models PHY service specifications, PPDU formats, PHY constants and PIB attributes. It currently only supports the transmit power spectral density mask specified in 2.4 GHz per section 6.5.3.1. The noise power density assumes uniformly distributed thermal noise across the frequency bands. The loss model can fully utilize all existing simple (non-spectrum phy) loss models. The Phy model uses the existing single spectrum channel model. The physical layer is modeled on packet level, that is, no preamble/SFD detection is done. Packet reception will be started with the first bit of the preamble (which is not modeled), if the SNR is more than -5 dB, see IEEE Std 802.15.4-2006, appendix E, Figure E.2. Reception of the packet will finish after the packet was completely transmitted. Other packets arriving during reception will add up to the interference/noise.

Currently the receiver sensitivity is set to a fixed value of -106.58 dBm. This corresponds to a packet error rate of 1% for 20 byte reference packets for this signal power, according to IEEE Std 802.15.4-2006, section 6.1.7. In the future we will provide support for changing the sensitivity to different values.

_images/802-15-4-per-sens.png

Packet error rate vs. signal power

NetDevice

Although it is expected that other technology profiles (such as 6LoWPAN and ZigBee) will write their own NetDevice classes, a basic LrWpanNetDevice is provided, which encapsulates the common operations of creating a generic LrWpan device and hooking things together.

Scope and Limitations

Future versions of this document will contain a PICS proforma similar to Appendix D of IEEE 802.15.4-2006. The current emphasis is on the unslotted mode of 802.15.4 operation for use in Zigbee, and the scope is limited to enabling a single mode (CSMA/CA) with basic data transfer capabilities. Association with PAN coordinators is not yet supported, nor the use of extended addressing. Interference is modeled as AWGN but this is currently not thoroughly tested.

The NetDevice Tx queue is not limited, i.e., packets are never dropped due to queue becoming full. They may be dropped due to excessive transmission retries or channel access failure.

References

  • Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs), IEEE Computer Society, IEEE Std 802.15.4-2006, 8 September 2006.
    1. Zheng and Myung J. Lee, “A comprehensive performance study of IEEE 802.15.4,” Sensor Network Operations, IEEE Press, Wiley Interscience, Chapter 4, pp. 218-237, 2006.

Usage

Enabling lr-wpan

Add lr-wpan to the list of modules built with ns-3.

Helper

The helper is patterned after other device helpers. In particular, tracing (ascii and pcap) is enabled similarly, and enabling of all lr-wpan log components is performed similarly. Use of the helper is exemplified in examples/lr-wpan-data.cc. For ascii tracing, the transmit and receive traces are hooked at the Mac layer.

The default propagation loss model added to the channel, when this helper is used, is the LogDistancePropagationLossModel with default parameters.

Examples

The following examples have been written, which can be found in src/lr-wpan/examples/:

  • lr-wpan-data.cc: A simple example showing end-to-end data transfer.
  • lr-wpan-error-distance-plot.cc: An example to plot variations of the packet success ratio as a function of distance.
  • lr-wpan-error-model-plot.cc: An example to test the phy.
  • lr-wpan-packet-print.cc: An example to print out the MAC header fields.
  • lr-wpan-phy-test.cc: An example to test the phy.

In particular, the module enables a very simplified end-to-end data transfer scenario, implemented in lr-wpan-data.cc. The figure shows a sequence of events that are triggered when the MAC receives a DataRequest from the higher layer. It invokes a Clear Channel Assessment (CCA) from the PHY, and if successful, sends the frame down to the PHY where it is transmitted over the channel and results in a DataIndication on the peer node.

_images/lr-wpan-data-example.png

Data example for simple LR-WPAN data transfer end-to-end

The example lr-wpan-error-distance-plot.cc plots the packet success ratio (PSR) as a function of distance, using the default LogDistance propagation loss model and the 802.15.4 error model. The channel (default 11), packet size (default 20 bytes) and transmit power (default 0 dBm) can be varied by command line arguments. The program outputs a file named 802.15.4-psr-distance.plt. Loading this file into gnuplot yields a file 802.15.4-psr-distance.eps, which can be converted to pdf or other formats. The default output is shown below.

_images/802-15-4-psr-distance.png

Default output of the program lr-wpan-error-distance-plot.cc

Tests

The following tests have been written, which can be found in src/lr-wpan/tests/:

  • lr-wpan-ack-test.cc: Check that acknowledgments are being used and issued in the correct order.
  • lr-wpan-collision-test.cc: Test correct reception of packets with interference and collisions.
  • lr-wpan-error-model-test.cc: Check that the error model gives predictable values.
  • lr-wpan-packet-test.cc: Test the 802.15.4 MAC header/trailer classes
  • lr-wpan-pd-plme-sap-test.cc: Test the PLME and PD SAP per IEEE 802.15.4
  • lr-wpan-spectrum-value-helper-test.cc: Test that the conversion between power (expressed as a scalar quantity) and spectral power, and back again, falls within a 25% tolerance across the range of possible channels and input powers.

Validation

The model has not been validated against real hardware. The error model has been validated against the data in IEEE Std 802.15.4-2006, section E.4.1.7 (Figure E.2). The MAC behavior (CSMA backoff) has been validated by hand against expected behavior. The below plot is an example of the error model validation and can be reproduced by running lr-wpan-error-model-plot.cc:

_images/802-15-4-ber.png

Default output of the program lr-wpan-error-model-plot.cc

LTE Module

Design Documentation

Overview

An overview of the LTE-EPC simulation model is depicted in the figure Overview of the LTE-EPC simulation model. There are two main components:

  • the LTE Model. This model includes the LTE Radio Protocol stack (RRC, PDCP, RLC, MAC, PHY). These entities reside entirely within the UE and the eNB nodes.
  • the EPC Model. This models includes core network interfaces, protocols and entities. These entities and protocols reside within the SGW, PGW and MME nodes, and partially within the eNB nodes.
_images/epc-topology.png

Overview of the LTE-EPC simulation model

Design Criteria

LTE Model

The LTE model has been designed to support the evaluation of the following aspects of LTE systems:

  • Radio Resource Management
  • QoS-aware Packet Scheduling
  • Inter-cell Interference Coordination
  • Dynamic Spectrum Access

In order to model LTE systems to a level of detail that is sufficient to allow a correct evaluation of the above mentioned aspects, the following requirements have been considered:

  1. At the radio level, the granularity of the model should be at least that of the Resource Block (RB). In fact, this is the fundamental unit being used for resource allocation. Without this minimum level of granularity, it is not possible to model accurately packet scheduling and inter-cell-interference. The reason is that, since packet scheduling is done on a per-RB basis, an eNB might transmit on a subset only of all the available RBs, hence interfering with other eNBs only on those RBs where it is transmitting. Note that this requirement rules out the adoption of a system level simulation approach, which evaluates resource allocation only at the granularity of call/bearer establishment.
  2. The simulator should scale up to tens of eNBs and hundreds of User Equipments (UEs). This rules out the use of a link level simulator, i.e., a simulator whose radio interface is modeled with a granularity up to the symbol level. This is because to have a symbol level model it is necessary to implement all the PHY layer signal processing, whose huge computational complexity severely limits simulation. In fact, link-level simulators are normally limited to a single eNB and one or a few UEs.
  3. It should be possible within the simulation to configure different cells so that they use different carrier frequencies and system bandwidths. The bandwidth used by different cells should be allowed to overlap, in order to support dynamic spectrum licensing solutions such as those described in [Ofcom2600MHz] and [RealWireless]. The calculation of interference should handle appropriately this case.
  4. To be more representative of the LTE standard, as well as to be as close as possible to real-world implementations, the simulator should support the MAC Scheduler API published by the FemtoForum [FFAPI]. This interface is expected to be used by femtocell manufacturers for the implementation of scheduling and Radio Resource Management (RRM) algorithms. By introducing support for this interface in the simulator, we make it possible for LTE equipment vendors and operators to test in a simulative environment exactly the same algorithms that would be deployed in a real system.
  5. The LTE simulation model should contain its own implementation of the API defined in [FFAPI]. Neither binary nor data structure compatibility with vendor-specific implementations of the same interface are expected; hence, a compatibility layer should be interposed whenever a vendor-specific MAC scheduler is to be used with the simulator. This requirement is necessary to allow the simulator to be independent from vendor-specific implementations of this interface specification. We note that [FFAPI] is a logical specification only, and its implementation (e.g., translation to some specific programming language) is left to the vendors.
  6. The model is to be used to simulate the transmission of IP packets by the upper layers. With this respect, it shall be considered that in LTE the Scheduling and Radio Resource Management do not work with IP packets directly, but rather with RLC PDUs, which are obtained by segmentation and concatenation of IP packets done by the RLC entities. Hence, these functionalities of the RLC layer should be modeled accurately.
EPC Model

The main objective of the EPC model is to provides means for the simulation of end-to-end IP connectivity over the LTE model. To this aim, it supports for the interconnection of multiple UEs to the Internet, via a radio access network of multiple eNBs connected to a single SGW/PGW node, as shown in Figure Overview of the LTE-EPC simulation model.

The following design choices have been made for the EPC model:

  1. The only Packet Data Network (PDN) type supported is IPv4.
  2. The SGW and PGW functional entities are implemented within a single node, which is hence referred to as the SGW/PGW node.
  3. The scenarios with inter-SGW mobility are not of interests. Hence, a single SGW/PGW node will be present in all simulations scenarios
  4. A requirement for the EPC model is that it can be used to simulate the end-to-end performance of realistic applications. Hence, it should be possible to use with the EPC model any regular ns-3 application working on top of TCP or UDP.
  5. Another requirement is the possibility of simulating network topologies with the presence of multiple eNBs, some of which might be equipped with a backhaul connection with limited capabilities. In order to simulate such scenarios, the user data plane protocols being used between the eNBs and the SGW/PGW should be modeled accurately.
  6. It should be possible for a single UE to use different applications with different QoS profiles. Hence, multiple EPS bearers should be supported for each UE. This includes the necessary classification of TCP/UDP traffic over IP done at the UE in the uplink and at the PGW in the downlink.
  7. The focus of the EPC model is mainly on the EPC data plane. The accurate modeling of the EPC control plane is, for the time being, not a requirement; hence, the necessary control plane interactions can be modeled in a simplified way by leveraging on direct interaction among the different simulation objects via the provided helper objects.
  8. The focus of the EPC model is on simulations of active users in ECM connected mode. Hence, all the functionality that is only relevant for ECM idle mode (in particular, tracking area update and paging) are not modeled at all.
  9. The model should allow the possibility to perform an X2-based handover between two eNBs.

Architecture

LTE Model
UE architecture

The architecture of the LTE radio protocol stack model of the UE is represented in the figures LTE radio protocol stack architecture for the UE on the data plane and LTE radio protocol stack architecture for the UE on the control plane which highlight respectively the data plane and the control plane.

_images/lte-arch-ue-data.png

LTE radio protocol stack architecture for the UE on the data plane

_images/lte-arch-ue-ctrl.png

LTE radio protocol stack architecture for the UE on the control plane

The architecture of the PHY/channel model of the UE is represented in figure PHY and channel model architecture for the UE.

_images/lte-ue-phy.png

PHY and channel model architecture for the UE

eNB architecture

The architecture of the LTE radio protocol stack model of the eNB is represented in the figures LTE radio protocol stack architecture for the eNB on the data plane and LTE radio protocol stack architecture for the eNB on the control plane which highlight respectively the data plane and the control plane.

_images/lte-arch-enb-data.png

LTE radio protocol stack architecture for the eNB on the data plane

_images/lte-arch-enb-ctrl.png

LTE radio protocol stack architecture for the eNB on the control plane

The architecture of the PHY/channel model of the eNB is represented in figure PHY and channel model architecture for the eNB.

_images/lte-enb-phy.png

PHY and channel model architecture for the eNB

EPC Model
EPC data plane

In Figure LTE-EPC data plane protocol stack, we represent the end-to-end LTE-EPC data plane protocol stack as it is modeled in the simulator. From the figure, it is evident that the biggest simplification introduced in the data plane model is the inclusion of the SGW and PGW functionality within a single SGW/PGW node, which removes the need for the S5 or S8 interfaces specified by 3GPP. On the other hand, for both the S1-U protocol stack and the LTE radio protocol stack all the protocol layers specified by 3GPP are present.

_images/lte-epc-e2e-data-protocol-stack.png

LTE-EPC data plane protocol stack

EPC control plane

The architecture of the implementation of the control plane model is shown in figure EPC control model. The control interfaces that are modeled explicitly are the S1-AP, the X2-AP and the S11 interfaces.

We note that the S1-AP and the S11 interfaces are modeled in a simplified fashion, by using just one pair of interface classes to model the interaction between entities that reside on different nodes (the eNB and the MME for the S1-AP interface, and the MME and the SGW for the S11 interface). In practice, this means that the primitives of these interfaces are mapped to a direct function call between the two objects. On the other hand, the X2-AP interface is being modeled using protocol data units sent over an X2 link (modeled as a point-to-point link); for this reason, the X2-AP interface model is more realistic.

_images/epc-ctrl-arch.png

EPC control model

Channel and Propagation

For channel modeling purposes, the LTE module uses the SpectrumChannel interface provided by the spectrum module. At the time of this writing, two implementations of such interface are available: SingleModelSpectrumChannel and MultiModelSpectrumChannel, and the LTE module requires the use of the MultiModelSpectrumChannel in order to work properly. This is because of the need to support different frequency and bandwidth configurations. All the the propagation models supported by MultiModelSpectrumChannel can be used within the LTE module.

Use of the Buildings model with LTE

The recommended propagation model to be used with the LTE module is the one provided by the Buildings module, which was in fact designed specifically with LTE (though it can be used with other wireless technologies as well). Please refer to the documentation of the Buildings module for generic information on the propagation model it provides.

In this section we will highlight some considerations that specifically apply when the Buildings module is used together with the LTE module.

The naming convention used in the following will be:

  • User equipment: UE
  • Macro Base Station: MBS
  • Small cell Base Station (e.g., pico/femtocell): SC

The LTE module considers FDD only, and implements downlink and uplink propagation separately. As a consequence, the following pathloss computations are performed

  • MBS <-> UE (indoor and outdoor)
  • SC (indoor and outdoor) <-> UE (indoor and outdoor)

The LTE model does not provide the following pathloss computations:

  • UE <-> UE
  • MBS <-> MBS
  • MBS <-> SC
  • SC <-> SC

The Buildings model does not know the actual type of the node; i.e., it is not aware of whether a transmitter node is a UE, a MBS, or a SC. Rather, the Buildings model only cares about the position of the node: whether it is indoor and outdoor, and what is its z-axis respect to the rooftop level. As a consequence, for an eNB node that is placed outdoor and at a z-coordinate above the rooftop level, the propagation models typical of MBS will be used by the Buildings module. Conversely, for an eNB that is placed outdoor but below the rooftop, or indoor, the propagation models typical of pico and femtocells will be used.

For communications involving at least one indoor node, the corresponding wall penetration losses will be calculated by the Buildings model. This covers the following use cases:

  • MBS <-> indoor UE
  • outdoor SC <-> indoor UE
  • indoor SC <-> indoor UE
  • indoor SC <-> outdoor UE

Please refer to the documentation of the Buildings module for details on the actual models used in each case.

Fading Model

The LTE module includes a trace-based fading model derived from the one developed during the GSoC 2010 [Piro2011]. The main characteristic of this model is the fact that the fading evaluation during simulation run-time is based on per-calculated traces. This is done to limit the computational complexity of the simulator. On the other hand, it needs huge structures for storing the traces; therefore, a trade-off between the number of possible parameters and the memory occupancy has to be found. The most important ones are:

  • users’ speed: relative speed between users (affects the Doppler frequency, which in turns affects the time-variance property of the fading)
  • number of taps (and relative power): number of multiple paths considered, which affects the frequency property of the fading.
  • time granularity of the trace: sampling time of the trace.
  • frequency granularity of the trace: number of values in frequency to be evaluated.
  • length of trace: ideally large as the simulation time, might be reduced by windowing mechanism.
  • number of users: number of independent traces to be used (ideally one trace per user).

With respect to the mathematical channel propagation model, we suggest the one provided by the rayleighchan function of Matlab, since it provides a well accepted channel modelization both in time and frequency domain. For more information, the reader is referred to [mathworks].

The simulator provides a matlab script (src/lte/model/fading-traces/fading-trace-generator.m) for generating traces based on the format used by the simulator. In detail, the channel object created with the rayleighchan function is used for filtering a discrete-time impulse signal in order to obtain the channel impulse response. The filtering is repeated for different TTI, thus yielding subsequent time-correlated channel responses (one per TTI). The channel response is then processed with the pwelch function for obtaining its power spectral density values, which are then saved in a file with the proper format compatible with the simulator model.

Since the number of variable it is pretty high, generate traces considering all of them might produce a high number of traces of huge size. On this matter, we considered the following assumptions of the parameters based on the 3GPP fading propagation conditions (see Annex B.2 of [TS36104]):

  • users’ speed: typically only a few discrete values are considered, i.e.:
    • 0 and 3 kmph for pedestrian scenarios
    • 30 and 60 kmph for vehicular scenarios
    • 0, 3, 30 and 60 for urban scenarios
  • channel taps: only a limited number of sets of channel taps are normally considered, for example three models are mentioned in Annex B.2 of [TS36104].
  • time granularity: we need one fading value per TTI, i.e., every 1 ms (as this is the granularity in time of the ns-3 LTE PHY model).
  • frequency granularity: we need one fading value per RB (which is the frequency granularity of the spectrum model used by the ns-3 LTE model).
  • length of the trace: the simulator includes the windowing mechanism implemented during the GSoC 2011, which consists of picking up a window of the trace each window length in a random fashion.
  • per-user fading process: users share the same fading trace, but for each user a different starting point in the trace is randomly picked up. This choice was made to avoid the need to provide one fading trace per user.

According to the parameters we considered, the following formula express in detail the total size S_{traces} of the fading traces:

S_{traces} = S_{sample} \times N_{RB} \times \frac{T_{trace}}{T_{sample}} \times N_{scenarios} \mbox{ [bytes]}

where S_{sample} is the size in bytes of the sample (e.g., 8 in case of double precision, 4 in case of float precision), N_{RB} is the number of RB or set of RBs to be considered, T_{trace} is the total length of the trace, T_{sample} is the time resolution of the trace (1 ms), and N_{scenarios} is the number of fading scenarios that are desired (i.e., combinations of different sets of channel taps and user speed values). We provide traces for 3 different scenarios one for each taps configuration defined in Annex B.2 of [TS36104]:

  • Pedestrian: with nodes’ speed of 3 kmph.
  • Vehicular: with nodes’ speed of 60 kmph.
  • Urban: with nodes’ speed of 3 kmph.

hence N_{scenarios} = 3. All traces have T_{trace} = 10 s and RB_{NUM} = 100. This results in a total 24 MB bytes of traces.

Antennas

Being based on the SpectrumPhy, the LTE PHY model supports antenna modeling via the ns-3 AntennaModel class. Hence, any model based on this class can be associated with any eNB or UE instance. For instance, the use of the CosineAntennaModel associated with an eNB device allows to model one sector of a macro base station. By default, the IsotropicAntennaModel is used for both eNBs and UEs.

PHY

Overview

The physical layer model provided in this LTE simulator is based on the one described in [Piro2011], with the following modifications. The model now includes the inter cell intereference calculation and the simulation of uplink traffic, including both packet transmission and CQI generation.

Subframe Structure

The subframe is divided into control and data part as described in Figure LTE subframe division..

_images/lte-subframe-structure.png

LTE subframe division.

Considering the granularity of the simulator based on RB, the control and the reference signaling have to be consequently modeled considering this constraint. According to the standard [TS36211], the downlink control frame starts at the beginning of each subframe and lasts up to three symbols across the whole system bandwidth, where the actual duration is provided by the Physical Control Format Indicator Channel (PCFICH). The information on the allocation are then mapped in the remaining resource up to the duration defined by the PCFICH, in the so called Physical Downlink Control Channel (PDCCH). A PDCCH transports a single message called Downlink Control Information (DCI) coming from the MAC layer, where the scheduler indicates the resource allocation for a specific user. The PCFICH and PDCCH are modeled with the transmission of the control frame of a fixed duration of 3/14 of milliseconds spanning in the whole available bandwidth, since the scheduler does not estimate the size of the control region. This implies that a single transmission block models the entire control frame with a fixed power (i.e., the one used for the PDSCH) across all the available RBs. According to this feature, this transmission represents also a valuable support for the Reference Signal (RS). This allows of having every TTI an evaluation of the interference scenario since all the eNB are transmitting (simultaneously) the control frame over the respective available bandwidths. We note that, the model does not include the power boosting since it does not reflect any improvement in the implemented model of the channel estimation.

The Sounding Reference Signal (SRS) is modeled similar to the downlink control frame. The SRS is periodically placed in the last symbol of the subframe in the whole system bandwidth. The RRC module already includes an algorithm for dynamically assigning the periodicity as function of the actual number of UEs attached to a eNB according to the UE-specific procedure (see Section 8.2 of [TS36213]).

MAC to Channel delay

To model the latency of real MAC and PHY implementations, the PHY model simulates a MAC-to-channel delay in multiples of TTIs (1ms). The transmission of both data and control packets are delayed by this amount.

CQI feedback

The generation of CQI feedback is done accordingly to what specified in [FFAPI]. In detail, we considered the generation of periodic wideband CQI (i.e., a single value of channel state that is deemed representative of all RBs in use) and inband CQIs (i.e., a set of value representing the channel state for each RB).

In downlink, the CQI feedbacks are currently evaluated according to the SINR perceived by control channel (i.e., PDCCH + PCFIC) in order to have an estimation of the interference when all the eNB are transmitting simultaneously. In uplink, two types of CQIs are implemented:

  • SRS based, periodically sent by the UEs.
  • PUSCH based, calculated from the actual transmitted data.

The scheduler interface include an attribute system calld UlCqiFilter for managing the filtering of the CQIs according to their nature, in detail:

  • SRS_UL_CQI for storing only SRS based CQIs.
  • PUSCH_UL_CQI for storing only PUSCH based CQIs.
  • ALL_UL_CQI for storing all the CQIs received.

It has to be noted that, the FfMacScheduler provides only the interface and it is matter of the actual scheduler implementation to include the code for managing these attibutes (see scheduler related section for more information on this matter).

Interference Model

The PHY model is based on the well-known Gaussian interference models, according to which the powers of interfering signals (in linear units) are summed up together to determine the overall interference power.

The sequence diagram of Figure Sequence diagram of the PHY interference calculation procedure shows how interfering signals are processed to calculate the SINR, and how SINR is then used for the generation of CQI feedback.

_images/lte-phy-interference.png

Sequence diagram of the PHY interference calculation procedure

LTE Spectrum Model

The usage of the radio spectrum by eNBs and UEs in LTE is described in [TS36101]. In the simulator, radio spectrum usage is modeled as follows. Let f_c denote the LTE Absolute Radio Frequency Channel Number, which identifies the carrier frequency on a 100 kHz raster; furthermore, let B be the Transmission Bandwidth Configuration in number of Resource Blocks. For every pair (f_c,B) used in the simulation we define a corresponding SpectrumModel using the functionality provided by the sec-spetrum-module . model using the Spectrum framework described in [Baldo2009]. f_c and B can be configured for every eNB instantiated in the simulation; hence, each eNB can use a different spectrum model. Every UE will automatically use the spectrum model of the eNB it is attached to. Using the MultiModelSpectrumChannel described in [Baldo2009], the interference among eNBs that use different spectrum models is properly accounted for. This allows to simulate dynamic spectrum access policies, such as for example the spectrum licensing policies that are discussed in [Ofcom2600MHz].

Data PHY Error Model

The simulator includes an error model of the data plane (i.e., PDSCH and PUSCH) according to the standard link-to-system mapping (LSM) techniques. The choice is aligned with the standard system simulation methodology of OFDMA radio transmission technology. Thanks to LSM we are able to maintain a good level of accuracy and at the same time limiting the computational complexity increase. It is based on the mapping of single link layer performance obtained by means of link level simulators to system (in our case network) simulators. In particular link the layer simulator is used for generating the performance of a single link from a PHY layer perspective, usually in terms of code block error rate (BLER), under specific static conditions. LSM allows the usage of these parameters in more complex scenarios, typical of system/network simulators, where we have more links, interference and “colored” channel propagation phenomena (e.g., frequency selective fading).

To do this the Vienna LTE Simulator [ViennaLteSim] has been used for what concerns the extraction of link layer performance and the Mutual Information Based Effective SINR (MIESM) as LSM mapping function using part of the work recently published by the Signet Group of University of Padua [PaduaPEM].

MIESM

The specific LSM method adopted is the one based on the usage of a mutual information metric, commonly referred to as the mutual information per per coded bit (MIB or MMIB when a mean of multiples MIBs is involved). Another option would be represented by the Exponential ESM (EESM); however, recent studies demonstrate that MIESM outperforms EESM in terms of accuracy [LozanoCost].

_images/miesm_scheme.png

MIESM computational procedure diagram

The mutual information (MI) is dependent on the constellation mapping and can be calculated per transport block (TB) basis, by evaluating the MI over the symbols and the subcarrier. However, this would be too complex for a network simulator. Hence, in our implementation a flat channel response within the RB has been considered; therefore the overall MI of a TB is calculated averaging the MI evaluated per each RB used in the TB. In detail, the implemented scheme is depicted in Figure MIESM computational procedure diagram, where we see that the model starts by evaluating the MI value for each RB, represented in the figure by the SINR samples. Then the equivalent MI is evaluated per TB basis by averaging the MI values. Finally, a further step has to be done since the link level simulator returns the performance of the link in terms of block error rate (BLER) in a addive white guassian noise (AWGN) channel, where the blocks are the code blocks (CBs) independently encoded/decoded by the turbo encoder. On this matter the standard 3GPP segmentation scheme has been used for estimating the actual CB size (described in section 5.1.2 of [TS36212]). This scheme divides the the TB in N_{K_-} blocks of size K_- and N_{K+} blocks of size K_+. Therefore the overall TB BLER (TBLER) can be expressed as

TBLER = 1- \prod\limits_{i=1}^{C}(1-CBLER_i)

where the CBLER_i is the BLER of the CB i obtained according to the link level simulator CB BLER curves. For estimating the CBLER_i, the MI evaluation has been implemented according to its numerical approximation defined in [wimaxEmd]. Moreover, for reducing the complexity of the computation, the approximation has been converted into lookup tables. In detail, Gaussian cumulative model has been used for approximating the AWGN BLER curves with three parameters which provides a close fit to the standard AWGN performances, in formula:

CBLER_i = \frac{1}{2}\left[1-erf\left(\frac{x-b_{ECR}}{\sqrt{2}c_{ECR}} \right) \right]

where x is the MI of the TB, b_{ECR} represents the “transition center” and c_{ECR} is related to the “transition width” of the Gaussian cumulative distribution for each Effective Code Rate (ECR) which is the actual transmission rate according to the channel coding and MCS. For limiting the computational complexity of the model we considered only a subset of the possible ECRs in fact we would have potentially 5076 possible ECRs (i.e., 27 MCSs and 188 CB sizes). On this respect, we will limit the CB sizes to some representative values (i.e., 40, 140, 160, 256, 512, 1024, 2048, 4032, 6144), while for the others the worst one approximating the real one will be used (i.e., the smaller CB size value available respect to the real one). This choice is aligned to the typical performance of turbo codes, where the CB size is not strongly impacting on the BLER. However, it is to be notes that for CB sizes lower than 1000 bits the effect might be relevant (i.e., till 2 dB); therefore, we adopt this unbalanced sampling interval for having more precision where it is necessary. This behaviour is confirmed by the figures presented in the Annes Section.

BLER Curves

On this respect, we reused part of the curves obtained within [PaduaPEM]. In detail, we introduced the CB size dependency to the CB BLER curves with the support of the developers of [PaduaPEM] and of the LTE Vienna Simulator. In fact, the module released provides the link layer performance only for what concerns the MCSs (i.e, with a given fixed ECR). In detail the new error rate curves for each has been evaluated with a simulation campaign with the link layer simulator for a single link with AWGN noise and for CB size of 104, 140, 256, 512, 1024, 2048, 4032 and 6144. These curves has been mapped with the Gaussian cumulative model formula presented above for obtaining the correspondents b_{ECR} and c_{ECR} parameters.

The BLER perfomance of all MCS obtained with the link level simulator are plotted in the following figures (blue lines) together with their correspondent mapping to the Gaussian cumulative distribution (red dashed lines).

_images/MCS_1_4.png

BLER for MCS 1, 2, 3 and 4.

_images/MCS_5_8.png

BLER for MCS 5, 6, 7 and 8.

_images/MCS_9_12.png

BLER for MCS 9, 10, 11 and 12.

_images/MCS_13_16.png

BLER for MCS 13, 14, 15 and 16.

_images/MCS_17_20.png

BLER for MCS 17, 17, 19 and 20.

_images/MCS_21_24.png

BLER for MCS 21, 22, 23 and 24.

_images/MCS_25_28.png

BLER for MCS 25, 26, 27 and 28.

_images/MCS_29_29.png

BLER for MCS 29.

Integration of the BLER curves in the ns-3 LTE module

The model implemented uses the curves for the LSM of the recently LTE PHY Error Model released in the ns3 community by the Signet Group [PaduaPEM] and the new ones generated for different CB sizes. The LteSpectrumPhy class is in charge of evaluating the TB BLER thanks to the methods provided by the LteMiErrorModel class, which is in charge of evaluating the TB BLER according to the vector of the perceived SINR per RB, the MCS and the size in order to proper model the segmentation of the TB in CBs. In order to obtain the vector of the perceived SINR two instances of LtePemSinrChunkProcessor (child of LteSinrChunkProcessor dedicated to evaluate the SINR for obtaining physical error performance) have been attached to UE downlink and eNB uplink LteSpectrumPhy modules for evaluating the error model distribution respectively of PDSCH (UE side) and ULSCH (eNB side).

The model can be disabled for working with a zero-losses channel by setting the PemEnabled attribute of the LteSpectrumPhy class (by default is active). This can be done according to the standard ns3 attribute system procedure, that is:

Config::SetDefault ("ns3::LteSpectrumPhy::DataErrorModelEnabled", BooleanValue (false));
Control Channels PHY Error Model

The simulator includes the error model for downlink control channels (PCFICH and PDCCH), while in uplink it is assumed and ideal error-free channel. The model is based on the MIESM approach presented before for considering the effects of the frequency selective channel since most of the control channels span the whole available bandwidth.

PCFICH + PDCCH Error Model

The model adopted for the error distribution of these channels is based on an evaluation study carried out in the RAN4 of 3GPP, where different vendors investigated the demodulation performance of the PCFICH jointly with PDCCH. This is due to the fact that the PCFICH is the channel in charge of communicating to the UEs the actual dimension of the PDCCH (which spans between 1 and 3 symbols); therefore the correct decodification of the DCIs depends on the correct interpretation of both ones. In 3GPP this problem have been evaluated for improving the cell-edge performance [FujitsuWhitePaper], where the interference among neighboring cells can be relatively high due to signal degradation. A similar problem has been notices in femto-cell scenario and, more in general, in HetNet scenarios the bottleneck has been detected mainly as the PCFICH channel [Bharucha2011], where in case of many eNBs are deployed in the same service area, this channel may collide in frequency, making impossible the correct detection of the PDCCH channel, too.

In the simulator, the SINR perceived during the reception has been estimated according to the MIESM model presented above in order to evaluate the error distribution of PCFICH and PDCCH. In detail, the SINR samples of all the RBs are included in the evaluation of the MI associated to the control frame and, according to this values, the effective SINR (eSINR) is obtained by inverting the MI evaluation process. It has to be noted that, in case of MIMO transmission, both PCFICH and the PDCCH use always the transmit diversity mode as defined by the standard. According to the eSINR perceived the decodification error probability can be estimated as function of the results presented in [R4-081920]. In case an error occur, the DCIs discarded and therefore the UE will be not able to receive the correspondent Tbs, therefore resulting lost.

MIMO Model

The use of multiple antennas both at transmitter and receiver side, known as multiple-input and multiple-output (MIMO), is a problem well studied in literature during the past years. Most of the work concentrate on evaluating analytically the gain that the different MIMO schemes might have in term of capacity; however someones provide also information of the gain in terms of received power [CatreuxMIMO].

According to the considerations above, a model more flexible can be obtained considering the gain that MIMO schemes bring in the system from a statistical point of view. As highlighted before, [CatreuxMIMO] presents the statistical gain of several MIMO solutions respect to the SISO one in case of no correlation between the antennas. In the work the gain is presented as the cumulative distribution function (CDF) of the output SINR for what concern SISO, MIMO-Alamouti, MIMO-MMSE, MIMO-OSIC-MMSE and MIMO-ZF schemes. Elaborating the results, the output SINR distribution can be approximated with a log-normal one with different mean and variance as function of the scheme considered. However, the variances are not so different and they are approximatively equal to the one of the SISO mode already included in the shadowing component of the BuildingsPropagationLossModel, in detail:

  • SISO: \mu = 13.5 and \sigma = 20 [dB].
  • MIMO-Alamouti: \mu = 17.7 and \sigma = 11.1 [dB].
  • MIMO-MMSE: \mu = 10.7 and \sigma = 16.6 [dB].
  • MIMO-OSIC-MMSE: \mu = 12.6 and \sigma = 15.5 [dB].
  • MIMO-ZF: \mu = 10.3 and \sigma = 12.6 [dB].

Therefore the PHY layer implements the MIMO model as the gain perceived by the receiver when using a MIMO scheme respect to the one obtained using SISO one. We note that, these gains referred to a case where there is no correlation between the antennas in MIMO scheme; therefore do not model degradation due to paths correlation.

UE PHY Measurements Model

According to [TS36214], the UE has to report a set of measurements of the eNBs that the device is able to perceive: the the reference signal received power (RSRP) and the reference signal received quality (RSRQ). The former is a measure of the received power of a specific eNB, while the latter includes also channel interference and thermal noise. The UE has to report the measurements jointly with the physical cell identity (PCI) of the cell. Both the RSRP and RSRQ measurements are performed during the reception of the RS, while the PCI is obtained with the Primary Synchronization Signal (PSS). The PSS is sent by the eNB each 5 subframes and in detail in the subframes 1 and 6. In real systems, only 504 distinct PCIs are available, and hence it could occur that two nearby eNBs use the same PCI; however, in the simulator we model PCIs using simulation metadata, and we allow up to 65535 distinct PCIs, thereby avoiding PCI collisions provided that less that 65535 eNBs are simulated in the same scenario.

According to [TS36133] sections 9.1.4 and 9.1.7, RSRP is reported by PHY layer in dBm while RSRQ in dB. The values of RSRP and RSRQ are provided to higher layers through the C-PHY SAP (by means of UeMeasurementsParameters struct) every 200 ms as defined in [TS36331]. Layer 1 filtering is performed by averaging the all the measurements collected during the last window slot. The periodicity of reporting can be adjusted for research purposes by means of the LteUePhy::UeMeasurementsFilterPeriod attribute.

The formulas of the RSRP and RSRQ can be simplified considering the assumption of the PHY layer that the channel is flat within the RB, the finest level of accuracy. In fact, this implies that all the REs within a RB have the same power, therefore:

RSRP = \frac{\sum_{k=0}^{K-1}\frac{\sum_{m=0}^{M-1}(P(k,m))}{M}}{K}
     = \frac{\sum_{k=0}^{K-1}\frac{(M \times P(k))}{M}}{K}
     = \frac{\sum_{k=0}^{K-1}(P(k))}{K}

where P(k,m) represents the signal power of the RE m within the RB k, which, as observed before, is constant within the same RB and equal to P(k), M is the number of REs carrying the RS in a RB and K is the number of RBs. It is to be noted that P(k), and in general all the powers defined in this section, is obtained in the simulator from the PSD of the RB (which is provided by the LteInterferencePowerChunkProcessor), in detail:

P(k) = PSD_{RB}(k)*180000/12

where PSD_{RB}(k) is the power spectral density of the RB k, 180000 is the bandwidth in Hz of the RB and 12 is the number of REs per RB in an OFDM symbol. Similarly, for RSSI we have

RSSI = \sum_{k=0}^{K-1} \frac{\sum_{s=0}^{S-1} \sum_{r=0}^{R-1}( P(k,s,r) + I(k,s,r) + N(k,s,r))}{S}

where S is the number of OFDM symbols carrying RS in a RB and R is the number of REs carrying a RS in a OFDM symbol (which is fixed to 2) while P(k,s,r), I(k,s,r) and N(k,s,r) represent respectively the perceived power of the serving cell, the interference power and the noise power of the RE r in symbol s. As for RSRP, the measurements within a RB are always equals among each others according to the PHY model; therefore P(k,s,r) = P(k), I(k,s,r) = I(k) and N(k,s,r) = N(k), which implies that the RSSI can be calculated as:

RSSI = \sum_{k=0}^{K-1} \frac{S \times 2 \times ( P(k) + I(k) + N(k))}{S}
     = \sum_{k=0}^{K-1} 2 \times ( P(k) + I(k) + N (k))

Considering the constraints of the PHY reception chain implementation, and in order to maintain the level of computational complexity low, only RSRP can be directly obtained for all the cells. This is due to the fact that LteSpectrumPhy is designed for evaluating the interference only respect to the signal of the serving eNB. This implies that the PHY layer is optimized for managing the power signals information with the serving eNB as a reference. However, RSRP and RSRQ of neighbor cell i can be extracted by the current information available of the serving cell j as detailed in the following:

RSRP_i = \frac{\sum_{k=0}^{K-1}(P_i(k))}{K}

RSSI_i = RSSI_j = \sum_{k=0}^{K-1} 2 \times ( I_j(k) + P_j(k) + N_j(k) )

RSRQ_i^j = K \times RSRP_i / RSSI_j

where RSRP_i is the RSRP of the neighbor cell i, P_i(k) is the power perceived at any RE within the RB k, K is the total number of RBs, RSSI_i is the RSSI of the neighbor cell i when the UE is attached to cell j (which, since it is the sum of all the received powers, coincides with RSSI_j), I_j(k) is the total interference perceived by UE in any RE of RB k when attached to cell i (obtained by the LteInterferencePowerChunkProcessor), P_j(k) is the power perceived of cell j in any RE of the RB k and N is the power noise spectral density in any RE. The sample is considered as valid in case of the RSRQ evaluated is above the LteUePhy::RsrqUeMeasThreshold attribute.

HARQ

The HARQ scheme implemented is based on a incremental redundancy (IR) solutions combined with multiple stop-and-wait processes for enabling a continuous data flow. In detail, the solution adopted is the soft combining hybrid IR Full incremental redundancy (also called IR Type II), which implies that the retransmissions contain only new information respect to the previous ones. The resource allocation algorithm of the HARQ has been implemented within the respective scheduler classes (i.e., RrFfMacScheduler and PfFfMacScheduler, refer to their correspondent sections for more info), while the decodification part of the HARQ has been implemented in the LteSpectrumPhy and LteHarqPhy classes which will be detailed in this section.

According to the standard, the UL retransmissions are synchronous and therefore are allocated 7 ms after the original transmission. On the other hand, for the DL, they are asynchronous and therefore can be allocated in a more flexible way starting from 7 ms and it is a matter of the specific scheduler implementation. The HARQ processes behavior is depicted in Figure:ref:fig-harq-processes-scheme.

At the MAC layer, the HARQ entity residing in the scheduler is in charge of controlling the 8 HARQ processes for generating new packets and managing the retransmissions both for the DL and the UL. The scheduler collects the HARQ feedback from eNB and UE PHY layers (respectively for UL and DL connection) by means of the FF API primitives SchedUlTriggerReq and SchedUlTriggerReq. According to the HARQ feedback and the RLC buffers status, the scheduler generates a set of DCIs including both retransmissions of HARQ blocks received erroneous and new transmissions, in general, giving priority to the former. On this matter, the scheduler has to take into consideration one constraint when allocating the resource for HARQ retransmissions, it must use the same modulation order of the first transmission attempt (i.e., QPSK for MCS \in [0..9], 16QAM for MCS \in [10..16] and 64QAM for MCS \in [17..28]). This restriction comes from the specification of the rate matcher in the 3GPP standard [TS36212], where the algorithm fixes the modulation order for generating the different blocks of the redundancy versions.

The PHY Error Model model (i.e., the LteMiErrorModel class already presented before) has been extended for considering IR HARQ according to [wimaxEmd], where the parameters for the AWGN curves mapping for MIESM mapping in case of retransmissions are given by:

R_{eff} = \frac{X}{\sum\limits_{i=1}^q C_i}

M_{I eff} = \frac{\sum\limits_{i=1}^q C_i M_i}{\sum\limits_{i=1}^q C_i}

where X is the number of original information bits, C_i are number of coded bits, M_i are the mutual informations per HARQ block received on the total number of q retransmissions. Therefore, in order to be able to return the error probability with the error model implemented in the simulator evaluates the R_{eff} and the MI_{I eff} and return the value of error probability of the ECR of the same modulation with closest lower rate respect to the R_{eff}. In order to consider the effect of HARQ retransmissions a new sets of curves have been integrated respect to the standard one used for the original MCS. The new curves are intended for covering the cases when the most conservative MCS of a modulation is used which implies the generation of R_{eff} lower respect to the one of standard MCSs. On this matter the curves for 1, 2 and 3 retransmissions have been evaluated for 10 and 17. For MCS 0 we considered only the first retransmission since the produced code rate is already very conservative (i.e., 0.04) and returns an error rate enough robust for the reception (i.e., the downturn of the BLER is centered around -18 dB). It is to be noted that, the size of first TB transmission has been assumed as containing all the information bits to be coded; therefore X is equal to the size of the first TB sent of a an HARQ process. The model assumes that the eventual presence of parity bits in the codewords is already considered in the link level curves. This implies that as soon as the minimum R_{eff} is reached the model is not including the gain due to the transmission of further parity bits.

_images/lte-harq-processes-scheme.png

HARQ processes behavior in LTE

The part of HARQ devoted to manage the decodification of the HARQ blocks has been implemented in the LteHarqPhy and LteSpectrumPhy classes. The former is in charge of maintaining the HARQ information for each active process . The latter interacts with LteMiErrorModel class for evaluating the correctness of the blocks received and includes the messaging algorithm in charge of communicating to the HARQ entity in the scheduler the result of the decodifications. These messages are encapsulated in the dlInfoListElement for DL and ulInfoListElement for UL and sent through the PUCCH and the PHICH respectively with an ideal error free model according to the assumptions in their implementation. A sketch of the iteration between HARQ and LTE protocol stack in represented in Figure:ref:fig-harq-architecture.

Finally, the HARQ engine is always active both at MAC and PHY layer; however, in case of the scheduler does not support HARQ the system will continue to work with the HARQ functions inhibited (i.e., buffers are filled but not used). This implementation characteristic gives backward compatibility with schedulers implemented before HARQ integration.

_images/lte-harq-architecture.png

Interaction between HARQ and LTE protocol stack

MAC

Resource Allocation Model

We now briefly describe how resource allocation is handled in LTE, clarifying how it is modeled in the simulator. The scheduler is in charge of generating specific structures called Data Control Indication (DCI) which are then transmitted by the PHY of the eNB to the connected UEs, in order to inform them of the resource allocation on a per subframe basis. In doing this in the downlink direction, the scheduler has to fill some specific fields of the DCI structure with all the information, such as: the Modulation and Coding Scheme (MCS) to be used, the MAC Transport Block (TB) size, and the allocation bitmap which identifies which RBs will contain the data transmitted by the eNB to each user.

For the mapping of resources to physical RBs, we adopt a localized mapping approach (see [Sesia2009], Section 9.2.2.1); hence in a given subframe each RB is always allocated to the same user in both slots. The allocation bitmap can be coded in different formats; in this implementation, we considered the Allocation Type 0 defined in [TS36213], according to which the RBs are grouped in Resource Block Groups (RBG) of different size determined as a function of the Transmission Bandwidth Configuration in use.

For certain bandwidth values not all the RBs are usable, since the group size is not a common divisor of the group. This is for instance the case when the bandwidth is equal to 25 RBs, which results in a RBG size of 2 RBs, and therefore 1 RB will result not addressable. In uplink the format of the DCIs is different, since only adjacent RBs can be used because of the SC-FDMA modulation. As a consequence, all RBs can be allocated by the eNB regardless of the bandwidth configuration.

Adaptive Modulation and Coding

The simulator provides two Adaptive Modulation and Coding (AMC) models: one based on the GSoC model [Piro2011] and one based on the physical error model (described in the following sections).

The former model is a modified version of the model described in [Piro2011], which in turn is inspired from [Seo2004]. Our version is described in the following. Let i denote the generic user, and let \gamma_i be its SINR. We get the spectral efficiency \eta_i of user i using the following equations:

\mathrm{BER} = 0.00005

\Gamma = \frac{ -\ln{ (5 * \mathrm{BER}) } }{ 1.5}

\eta_i = \log_2 { \left( 1 + \frac{ {\gamma}_i }{ \Gamma } \right)}

The procedure described in [R1-081483] is used to get the corresponding MCS scheme. The spectral efficiency is quantized based on the channel quality indicator (CQI), rounding to the lowest value, and is mapped to the corresponding MCS scheme.

Finally, we note that there are some discrepancies between the MCS index in [R1-081483] and that indicated by the standard: [TS36213] Table 7.1.7.1-1 says that the MCS index goes from 0 to 31, and 0 appears to be a valid MCS scheme (TB size is not 0) but in [R1-081483] the first useful MCS index is 1. Hence to get the value as intended by the standard we need to subtract 1 from the index reported in [R1-081483].

The alternative model is based on the physical error model developed for this simulator and explained in the following subsections. This scheme is able to adapt the MCS selection to the actual PHY layer performance according to the specific CQI report. According to their definition, a CQI index is assigned when a single PDSCH TB with the modulation coding scheme and code rate correspondent to that CQI index in table 7.2.3-1 of [TS36213] can be received with an error probability less than 0.1. In case of wideband CQIs, the reference TB includes all the RBGs available in order to have a reference based on the whole available resources; while, for subband CQIs, the reference TB is sized as the RBGs.

Transport Block model

The model of the MAC Transport Blocks (TBs) provided by the simulator is simplified with respect to the 3GPP specifications. In particular, a simulator-specific class (PacketBurst) is used to aggregate MAC SDUs in order to achieve the simulator’s equivalent of a TB, without the corresponding implementation complexity. The multiplexing of different logical channels to and from the RLC layer is performed using a dedicated packet tag (LteRadioBearerTag), which performs a functionality which is partially equivalent to that of the MAC headers specified by 3GPP.

The FemtoForum MAC Scheduler Interface

This section describes the ns-3 specific version of the LTE MAC Scheduler Interface Specification published by the FemtoForum [FFAPI].

We implemented the ns-3 specific version of the FemtoForum MAC Scheduler Interface [FFAPI] as a set of C++ abstract classes; in particular, each primitive is translated to a C++ method of a given class. The term implemented here is used with the same meaning adopted in [FFAPI], and hence refers to the process of translating the logical interface specification to a particular programming language. The primitives in [FFAPI] are grouped in two groups: the CSCHED primitives, which deal with scheduler configuration, and the SCHED primitives, which deal with the execution of the scheduler. Furthermore, [FFAPI] defines primitives of two different kinds: those of type REQ go from the MAC to the Scheduler, and those of type IND/CNF go from the scheduler to the MAC. To translate these characteristics into C++, we define the following abstract classes that implement Service Access Points (SAPs) to be used to issue the primitives:

  • the FfMacSchedSapProvider class defines all the C++ methods that correspond to SCHED primitives of type REQ;
  • the FfMacSchedSapUser class defines all the C++ methods that correspond to SCHED primitives of type CNF/IND;
  • the FfMacCschedSapProvider class defines all the C++ methods that correspond to CSCHED primitives of type REQ;
  • the FfMacCschedSapUser class defines all the C++ methods that correspond to CSCHED primitives of type CNF/IND;

There are 3 blocks involved in the MAC Scheduler interface: Control block, Subframe block and Scheduler block. Each of these blocks provide one part of the MAC Scheduler interface. The figure below shows the relationship between the blocks and the SAPs defined in our implementation of the MAC Scheduler Interface.

_images/ff-mac-saps.png

In addition to the above principles, the following design choices have been taken:

  • The definition of the MAC Scheduler interface classes follows the naming conventions of the ns-3 Coding Style. In particular, we follow the CamelCase convention for the primitive names. For example, the primitive CSCHED_CELL_CONFIG_REQ is translated to CschedCellConfigReq in the ns-3 code.
  • The same naming conventions are followed for the primitive parameters. As the primitive parameters are member variables of classes, they are also prefixed with a m_.
  • regarding the use of vectors and lists in data structures, we note that [FFAPI] is a pretty much C-oriented API. However, considered that C++ is used in ns-3, and that the use of C arrays is discouraged, we used STL vectors (std::vector) for the implementation of the MAC Scheduler Interface, instead of using C arrays as implicitly suggested by the way [FFAPI] is written.
  • In C++, members with constructors and destructors are not allow in unions. Hence all those data structures that are said to be unions in [FFAPI] have been defined as structs in our code.

The figure below shows how the MAC Scheduler Interface is used within the eNB.

_images/ff-example.png

The User side of both the CSCHED SAP and the SCHED SAP are implemented within the eNB MAC, i.e., in the file lte-enb-mac.cc. The eNB MAC can be used with different scheduler implementations without modifications. The same figure also shows, as an example, how the Round Robin Scheduler is implemented: to interact with the MAC of the eNB, the Round Robin scheduler implements the Provider side of the SCHED SAP and CSCHED SAP interfaces. A similar approach can be used to implement other schedulers as well. A description of each of the scheduler implementations that we provide as part of our LTE simulation module is provided in the following subsections.

Round Robin (RR) Scheduler

The Round Robin (RR) scheduler is probably the simplest scheduler found in the literature. It works by dividing the available resources among the active flows, i.e., those logical channels which have a non-empty RLC queue. If the number of RBGs is greater than the number of active flows, all the flows can be allocated in the same subframe. Otherwise, if the number of active flows is greater than the number of RBGs, not all the flows can be scheduled in a given subframe; then, in the next subframe the allocation will start from the last flow that was not allocated. The MCS to be adopted for each user is done according to the received wideband CQIs.

For what concern the HARQ, RR implements the non adaptive version, which implies that in allocating the retransmission attempts RR uses the same allocation configuration of the original block, which means maintaining the same RBGs and MCS. UEs that are allocated for HARQ retransmissions are not considered for the transmission of new data in case they have a transmission opportunity available in the same TTI. Finally, HARQ can be disabled with ns3 attribute system for maintaining backward compatibility with old test cases and code, in detail:

Config::SetDefault ("ns3::RrFfMacScheduler::HarqEnabled", BooleanValue (false));

The scheduler implements the filtering of the uplink CQIs according to their nature with UlCqiFilter attibute, in detail:

  • SRS_UL_CQI: only SRS based CQI are stored in the internal attributes.
  • PUSCH_UL_CQI: only PUSCH based CQI are stored in the internal attributes.
  • ALL_UL_CQI: all CQIs are stored in the same internal attibute (i.e., the last CQI received is stored independently from its nature).
Proportional Fair (PF) Scheduler

The Proportional Fair (PF) scheduler [Sesia2009] works by scheduling a user when its instantaneous channel quality is high relative to its own average channel condition over time. Let i,j denote generic users; let t be the subframe index, and k be the resource block index; let M_{i,k}(t) be MCS usable by user i on resource block k according to what reported by the AMC model (see Adaptive Modulation and Coding); finally, let S(M, B) be the TB size in bits as defined in [TS36213] for the case where a number B of resource blocks is used. The achievable rate R_{i}(k,t) in bit/s for user i on resource block group k at subframe t is defined as

R_{i}(k,t) =  \frac{S\left( M_{i,k}(t), 1\right)}{\tau}

where \tau is the TTI duration. At the start of each subframe t, each RBG is assigned to a certain user. In detail, the index \widehat{i}_{k}(t) to which RBG k is assigned at time t is determined as

\widehat{i}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
 \left( \frac{ R_{j}(k,t) }{ T_\mathrm{j}(t) } \right)

where T_{j}(t) is the past througput performance perceived by the user j. According to the above scheduling algorithm, a user can be allocated to different RBGs, which can be either adjacent or not, depending on the current condition of the channel and the past throughput performance T_{j}(t). The latter is determined at the end of the subframe t using the following exponential moving average approach:

T_{j}(t) =
(1-\frac{1}{\alpha})T_{j}(t-1)
+\frac{1}{\alpha} \widehat{T}_{j}(t)

where \alpha is the time constant (in number of subframes) of the exponential moving average, and \widehat{T}_{j}(t) is the actual throughput achieved by the user i in the subframe t. \widehat{T}_{j}(t) is measured according to the following procedure. First we determine the MCS \widehat{M}_j(t) actually used by user j:

\widehat{M}_j(t) = \min_{k: \widehat{i}_{k}(t) = j}{M_{j,k}(t)}

then we determine the total number \widehat{B}_j(t) of RBGs allocated to user j:

\widehat{B}_j(t) = \left| \{ k :  \widehat{i}_{k}(t) = j \} \right|

where |\cdot| indicates the cardinality of the set; finally,

\widehat{T}_{j}(t) = \frac{S\left( \widehat{M}_j(t), \widehat{B}_j(t)
\right)}{\tau}

For what concern the HARQ, PF implements the non adaptive version, which implies that in allocating the retransmission attempts the scheduler uses the same allocation configuration of the original block, which means maintaining the same RBGs and MCS. UEs that are allocated for HARQ retransmissions are not considered for the transmission of new data in case they have a transmission opportunity available in the same TTI. Finally, HARQ can be disabled with ns3 attribute system for maintaining backward compatibility with old test cases and code, in detail:

Config::SetDefault ("ns3::PfFfMacScheduler::HarqEnabled", BooleanValue (false));
Maximum Throughput (MT) Scheduler

The Maximum Throughput (MT) scheduler [FCapo2012] aims to maximize the overall throughput of eNB. It allocates each RB to the user that can achieve the maximum achievable rate in the current TTI. Currently, MT scheduler in NS-3 has two versions: frequency domain (FDMT) and time domain (TDMT). In FDMT, every TTI, MAC scheduler allocates RBGs to the UE who has highest achievable rate calculated by subband CQI. In TDMT, every TTI, MAC scheduler selects one UE which has highest achievable rate calculated by wideband CQI. Then MAC scheduler allocates all RBGs to this UE in current TTI. The calculation of achievable rate in FDMT and TDMT is as same as the one in PF. Let i,j denote generic users; let t be the subframe index, and k be the resource block index; let M_{i,k}(t) be MCS usable by user i on resource block k according to what reported by the AMC model (see Adaptive Modulation and Coding); finally, let S(M, B) be the TB size in bits as defined in [TS36213] for the case where a number B of resource blocks is used. The achievable rate R_{i}(k,t) in bit/s for user i on resource block k at subframe t is defined as

R_{i}(k,t) =  \frac{S\left( M_{i,k}(t), 1\right)}{\tau}

where \tau is the TTI duration. At the start of each subframe t, each RB is assigned to a certain user. In detail, the index \widehat{i}_{k}(t) to which RB k is assigned at time t is determined as

\widehat{i}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
    \left( { R_{j}(k,t) } \right)

When there are several UEs having the same achievable rate, current implementation always selects the first UE created in script. Although MT can maximize cell throughput, it cannot provide fairness to UEs in poor channel condition.

Throughput to Average (TTA) Scheduler

The Throughput to Average (TTA) scheduler [FCapo2012] can be considered as an intermediate between MT and PF. The metric used in TTA is calculated as follows:

\widehat{i}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
 \left( \frac{ R_{j}(k,t) }{ R_{j}(t) } \right)

Here, R_{i}(k,t) in bit/s represents the achievable rate for user i on resource block k at subframe t. The calculation method already is shown in MT and PF. Meanwhile, R_{i}(t) in bit/s stands for the achievable rate for i at subframe t. The difference between those two achievable rates is how to get MCS. For R_{i}(k,t), MCS is calculated by subband CQI while R_{i}(t) is calculated by wideband CQI. TTA scheduler can only be implemented in frequency domain (FD) because the achievable rate of particular RBG is only related to FD scheduling.

Blind Average Throughput Scheduler

The Blind Average Throughput scheduler [FCapo2012] aims to provide equal throughput to all UEs under eNB. The metric used in TTA is calculated as follows:

\widehat{i}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
 \left( \frac{ 1 }{ T_\mathrm{j}(t) } \right)

where T_{j}(t) is the past throughput performance perceived by the user j and can be calculated by the same method in PF scheduler. In the time domain blind average throughput (TD-BET), the scheduler selects the UE with largest priority metric and allocates all RBGs to this UE. On the other hand, in the frequency domain blind average throughput (FD-BET), every TTI, the scheduler first selects one UE with lowest pastAverageThroughput (largest priority metric). Then scheduler assigns one RBG to this UE, it calculates expected throughput of this UE and uses it to compare with past average throughput T_{j}(t) of other UEs. The scheduler continues to allocate RBG to this UE until its expected throughput is not the smallest one among past average throughput T_{j}(t) of all UE. Then the scheduler will use the same way to allocate RBG for a new UE which has the lowest past average throughput T_{j}(t) until all RBGs are allocated to UEs. The principle behind this is that, in every TTI, the scheduler tries the best to achieve the equal throughput among all UEs.

Token Bank Fair Queue Scheduler

Token Bank Fair Queue (TBFQ) is a QoS aware scheduler which derives from the leaky-bucket mechanism. In TBFQ, a traffic flow of user i is characterized by following parameters:

  • t_{i}: packet arrival rate (byte/sec )
  • r_{i}: token generation rate (byte/sec)
  • p_{i}: token pool size (byte)
  • E_{i}: counter that records the number of token borrowed from or given to the token bank by flow i ; E_{i} can be smaller than zero

Each K bytes data consumes k tokens. Also, TBFQ maintains a shared token bank (B) so as to balance the traffic between different flows. If token generation rate r_{i} is bigger than packet arrival rate t_{i}, then tokens overflowing from token pool are added to the token bank, and E_{i} is increased by the same amount. Otherwise, flow i needs to withdraw tokens from token bank based on a priority metric frac{E_{i}}{r_{i}}, and E_{i} is decreased. Obviously, the user contributes more on token bank has higher priority to borrow tokens; on the other hand, the user borrows more tokens from bank has lower priority to continue to withdraw tokens. Therefore, in case of several users having the same token generation rate, traffic rate and token pool size, user suffers from higher interference has more opportunity to borrow tokens from bank. In addition, TBFQ can police the traffic by setting the token generation rate to limit the throughput. Additionally, TBFQ also maintains following three parameters for each flow:

  • Debt limit d_{i}: if E_{i} belows this threshold, user i cannot further borrow tokens from bank. This is for preventing malicious UE to borrow too much tokens.
  • Credit limit c_{i}: the maximum number of tokens UE i can borrow from the bank in one time.
  • Credit threshold C: once E_{i} reaches debt limit, UE i must store C tokens to bank in order to further borrow token from bank.

LTE in NS-3 has two versions of TBFQ scheduler: frequency domain TBFQ (FD-TBFQ) and time domain TBFQ (TD-TBFQ). In FD-TBFQ, the scheduler always select UE with highest metric and allocates RBG with highest subband CQI until there are no packets within UE’s RLC buffer or all RBGs are allocated [FABokhari2009]. In TD-TBFQ, after selecting UE with maximum metric, it allocates all RBGs to this UE by using wideband CQI [WKWong2004].

Priority Set Scheduler

Priority set scheduler (PSS) is a QoS aware scheduler which combines time domain (TD) and frequency domain (FD) packet scheduling operations into one scheduler [GMonghal2008]. It controls the fairness among UEs by a specified Target Bit Rate (TBR).

In TD scheduler part, PSS first selects UEs with non-empty RLC buffer and then divide them into two sets based on the TBR:

  • set 1: UE whose past average throughput is smaller than TBR; TD scheduler calculates their priority metric in Blind Equal Throughput (BET) style:

\widehat{i}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
 \left( \frac{ 1 }{ T_\mathrm{j}(t) } \right)

  • set 2: UE whose past average throughput is larger (or equal) than TBR; TD scheduler calculates their priority metric in Proportional Fair (PF) style:

\widehat{i}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
 \left( \frac{ R_{j}(k,t) }{ T_\mathrm{j}(t) } \right)

UEs belonged to set 1 have higher priority than ones in set 2. Then PSS will select N_{mux} UEs with highest metric in two sets and forward those UE to FD scheduler. In PSS, FD scheduler allocates RBG k to UE n that maximums the chosen metric. Two PF schedulers are used in PF scheduler:

  • Proportional Fair scheduled (PFsch)

\widehat{Msch}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
 \left( \frac{ R_{j}(k,t) }{ Tsch_\mathrm{j}(t) } \right)

  • Carrier over Interference to Average (CoIta)

\widehat{Mcoi}_{k}(t) = \underset{j=1,...,N}{\operatorname{argmax}}
 \left( \frac{ CoI[j,k] }{ \sum_{k=0}^{N_{RBG}} CoI[j,k] } \right)

where Tsch_{j}(t) is similar past throughput performance perceived by the user j, with the difference that it is updated only when the i-th user is actually served. CoI[j,k] is an estimation of the SINR on the RBG k of UE j. Both PFsch and CoIta is for decoupling FD metric from TD scheduler. In addition, PSS FD scheduler also provide a weight metric W[n] for helping controlling fairness in case of low number of UEs.

W[n] =  max (1, \frac{TBR}{ T_{j}(t) })

where T_{j}(t) is the past throughput performance perceived by the user j . Therefore, on RBG k, the FD scheduler selects the UE j that maximizes the product of the frequency domain metric (Msch, MCoI) by weight W[n]. This strategy will guarantee the throughput of lower quality UE tend towards the TBR.

Config::SetDefault ("ns3::PfFfMacScheduler::HarqEnabled", BooleanValue (false));

The scheduler implements the filtering of the uplink CQIs according to their nature with UlCqiFilter attibute, in detail:

  • SRS_UL_CQI: only SRS based CQI are stored in the internal attributes.
  • PUSCH_UL_CQI: only PUSCH based CQI are stored in the internal attributes.
  • ALL_UL_CQI: all CQIs are stored in the same internal attibute (i.e., the last CQI received is stored independently from its nature).
Channel and QoS Aware Scheduler

The Channel and QoS Aware (CQA) Scheduler [Bbojovic2014] is an LTE MAC downlink scheduling algorithm that considers the head of line (HOL) delay, the GBR parameters and channel quality over different subbands. The CQA scheduler is based on joint TD and FD scheduling.

In the TD (at each TTI) the CQA scheduler groups users by priority. The purpose of grouping is to enforce the FD scheduling to consider first the flows with highest HOL delay. The grouping metric m_{td} for user j=1,...,N is defined in the following way:

m_{td}^{j}(t) = \lceil\frac{d_{hol}^{j}(t)}{g}\rceil \;,

where d_{hol}^{j}(t) is the current value of HOL delay of flow j, and g is a grouping parameter that determines granularity of the groups, i.e. the number of the flows that will be considered in the FD scheduling iteration.

The groups of flows selected in the TD iteration are forwarded to the FD scheduling starting from the flows with the highest value of the m_{td} metric until all RBGs are assigned in the corresponding TTI. In the FD, for each RBG k=1,...,K, the CQA scheduler assigns the current RBG to the user j that has the maximum value of the FD metric which we define in the following way:

m_{fd}^{(k,j)}(t) = d_{HOL}^{j}(t) \cdot m_{GBR}^j(t) \cdot m_{ca}^{k,j}(t) \;,

where m_{GBR}^j(t) is calculated as follows:

m_{GBR}^j(t)=\frac{GBR^j}{\overline{R^j}(t)}=\frac{GBR^j}{(1-\alpha)\cdot\overline{R^j}(t-1)+\alpha \cdot r^j(t)} \;,

where GBR^j is the bit rate specified in EPS bearer of the flow j, \overline{R^j}(t) is the past averaged throughput that is calculated with a moving average, r^{j}(t) is the throughput achieved at the time t, and \alpha is a coefficient such that 0 \le \alpha
\le1.

For m_{ca}^{(k,j)}(t) we consider two different metrics: m_{pf}^{(k,j)}(t) and m_{ff}^{(k,j)}(t). m_{pf} is the Proportional Fair metric which is defined as follows:

m_{pf}^{(k,j)}(t) = \frac{R_e^{(k,j)}}{\overline{R^j}(t)} \;,

where R_e^{(k,j)}(t) is the estimated achievable throughput of user j over RBG k calculated by the Adaptive Modulation and Coding (AMC) scheme that maps the channel quality indicator (CQI) value to the transport block size in bits.

The other channel awareness metric that we consider is m_{ff} which is proposed in [GMonghal2008] and it represents the frequency selective fading gains over RBG k for user j and is calculated in the following way:

m_{ff}^{(k,j)}(t) = \frac{CQI^{(k,j)}(t)}{\sum_{k=1}^{K}CQI(t)^{(k,j)}} \;,

where CQI^{(k,j)}(t) is the last reported CQI value from user j for the k-th RBG.

The user can select whether m_{pf} or m_{ff} is used by setting the attribute ns3::CqaFfMacScheduler::CqaMetric respectively to "CqaPf" or "CqaFf".

Random Access

The LTE model includes a model of the Random Access procedure based on some simplifying assumptions, which are detailed in the following for each of the messages and signals described in the specs [TS36321].

  • Random Access (RA) preamble: in real LTE systems this corresponds to a Zadoff-Chu (ZC) sequence using one of several formats available and sent in the PRACH slots which could in principle overlap with PUSCH. The RA preamble is modeled using the LteControlMessage class, i.e., as an ideal message that does not consume any radio resources. The collision of preamble transmission by multiple UEs in the same cell are modeled using a protocol interference model, i.e., whenever two or more identical preambles are transmitted in same cell at the same TTI, no one of these identical preambles will be received by the eNB. Other than this collision model, no error model is associated with the reception of a RA preamble.
  • Random Access Response (RAR): in real LTE systems, this is a special MAC PDU sent on the DL-SCH. Since MAC control elements are not accurately modeled in the simulator (only RLC and above PDUs are), the RAR is modeled as an LteControlMessage that does not consume any radio resources. Still, during the RA procedure, the LteEnbMac will request to the scheduler the allocation of resources for the RAR using the FF MAC Scheduler primitive SCHED_DL_RACH_INFO_REQ. Hence, an enhanced scheduler implementation (not available at the moment) could allocate radio resources for the RAR, thus modeling the consumption of Radio Resources for the transmission of the RAR.
  • Message 3: in real LTE systems, this is an RLC TM SDU sent over resources specified in the UL Grant in the RAR. In the simulator, this is modeled as a real RLC TM RLC PDU whose UL resources are allocated by the scheduler upon call to SCHED_DL_RACH_INFO_REQ.
  • Contention Resolution (CR): in real LTE system, the CR phase is needed to address the case where two or more UE sent the same RA preamble in the same TTI, and the eNB was able to detect this preamble in spite of the collision. Since this event does not occur due to the protocol interference model used for the reception of RA preambles, the CR phase is not modeled in the simulator, i.e., the CR MAC CE is never sent by the eNB and the UEs consider the RA to be successful upon reception of the RAR. As a consequence, the radio resources consumed for the transmission of the CR MAC CE are not modeled.

Figure Sequence diagram of the Contention-based MAC Random Access procedure and Sequence diagram of the Non-contention-based MAC Random Access procedure shows the sequence diagrams of respectively the contention-based and non-contention-based MAC random access procedure, highlighting the interactions between the MAC and the other entities.

_images/mac-random-access-contention.png

Sequence diagram of the Contention-based MAC Random Access procedure

_images/mac-random-access-noncontention.png

Sequence diagram of the Non-contention-based MAC Random Access procedure

RLC

Overview

The RLC entity is specified in the 3GPP technical specification [TS36322], and comprises three different types of RLC: Transparent Mode (TM), Unacknowledge Mode (UM) and Acknowledged Mode (AM). The simulator includes one model for each of these entitities

The RLC entities provide the RLC service interface to the upper PDCP layer and the MAC service interface to the lower MAC layer. The RLC entities use the PDCP service interface from the upper PDCP layer and the MAC service interface from the lower MAC layer.

Figure Implementation Model of PDCP, RLC and MAC entities and SAPs shows the implementation model of the RLC entities and its relationship with all the other entities and services in the protocol stack.

_images/lte-rlc-implementation-model.png

Implementation Model of PDCP, RLC and MAC entities and SAPs

Service Interfaces
RLC Service Interface

The RLC service interface is divided into two parts:

  • the RlcSapProvider part is provided by the RLC layer and used by the upper PDCP layer and
  • the RlcSapUser part is provided by the upper PDCP layer and used by the RLC layer.

Both the UM and the AM RLC entities provide the same RLC service interface to the upper PDCP layer.

RLC Service Primitives

The following list specifies which service primitives are provided by the RLC service interfaces:

  • RlcSapProvider::TransmitPdcpPdu

    • The PDCP entity uses this primitive to send a PDCP PDU to the lower RLC entity in the transmitter peer
  • RlcSapUser::ReceivePdcpPdu

    • The RLC entity uses this primitive to send a PDCP PDU to the upper PDCP entity in the receiver peer
MAC Service Interface

The MAC service interface is divided into two parts:

  • the MacSapProvider part is provided by the MAC layer and used by the upper RLC layer and
  • the MacSapUser part is provided by the upper RLC layer and used by the MAC layer.
MAC Service Primitives

The following list specifies which service primitives are provided by the MAC service interfaces:

  • MacSapProvider::TransmitPdu

    • The RLC entity uses this primitive to send a RLC PDU to the lower MAC entity in the transmitter peer
  • MacSapProvider::ReportBufferStatus

    • The RLC entity uses this primitive to report the MAC entity the size of pending buffers in the transmitter peer
  • MacSapUser::NotifyTxOpportunity

    • The MAC entity uses this primitive to nofify the RLC entity a transmission opportunity
  • MacSapUser::ReceivePdu

    • The MAC entity uses this primitive to send an RLC PDU to the upper RLC entity in the receiver peer
AM RLC

The processing of the data transfer in the Acknowledge Mode (AM) RLC entity is explained in section 5.1.3 of [TS36322]. In this section we describe some details of the implementation of the RLC entity.

Buffers for the transmit operations

Our implementation of the AM RLC entity maintains 3 buffers for the transmit operations:

  • Transmission Buffer: it is the RLC SDU queue. When the AM RLC entity receives a SDU in the TransmitPdcpPdu service primitive from the upper PDCP entity, it enqueues it in the Transmission Buffer. We put a limit on the RLC buffer size and just silently drop SDUs when the buffer is full.
  • Transmitted PDUs Buffer: it is the queue of transmitted RLC PDUs for which an ACK/NACK has not been received yet. When the AM RLC entity sends a PDU to the MAC entity, it also puts a copy of the transmitted PDU in the Transmitted PDUs Buffer.
  • Retransmission Buffer: it is the queue of RLC PDUs which are considered for retransmission (i.e., they have been NACKed). The AM RLC entity moves this PDU to the Retransmission Buffer, when it retransmits a PDU from the Transmitted Buffer.
Calculation of the buffer size

The Transmission Buffer contains RLC SDUs. A RLC PDU is one or more SDU segments plus an RLC header. The size of the RLC header of one RLC PDU depends on the number of SDU segments the PDU contains.

The 3GPP standard (section 6.1.3.1 of [TS36321]) says clearly that, for the uplink, the RLC and MAC headers are not considered in the buffer size that is to be report as part of the Buffer Status Report. For the downlink, the behavior is not specified. Neither [FFAPI] specifies how to do it. Our RLC model works by assuming that the calculation of the buffer size in the downlink is done exactly as in the uplink, i.e., not considering the RLC and MAC header size.

We note that this choice affects the interoperation with the MAC scheduler, since, in response to the Notify_Tx_Opportunity service primitive, the RLC is expected to create a PDU of no more than the size requested by the MAC, including RLC overhead. Hence, unneeded fragmentation can occur if (for example) the MAC notifies a transmission exactly equal to the buffer size previously reported by the RLC. We assume that it is left to the Scheduler to implement smart strategies for the selection of the size of the transmission opportunity, in order to eventually avoid the inefficiency of unneeded fragmentation.

Concatenation and Segmentation

The AM RLC entity generates and sends exactly one RLC PDU for each transmission opportunity even if it is smaller than the size reported by the transmission opportunity. So for instance, if a STATUS PDU is to be sent, then only this PDU will be sent in that transmission opportunity.

The segmentation and concatenation for the SDU queue of the AM RLC entity follows the same philosophy as the same procedures of the UM RLC entity but there are new state variables (see [TS36322] section 7.1) only present in the AM RLC entity.

It is noted that, according to the 3GPP specs, there is no concatenation for the Retransmission Buffer.

Re-segmentation

The current model of the AM RLC entity does not support the re-segmentation of the retransmission buffer. Rather, the AM RLC entity just waits to receive a big enough transmission opportunity.

Unsupported features

We do not support the following procedures of [TS36322] :

  • “Send an indication of successful delivery of RLC SDU” (See section 5.1.3.1.1)
  • “Indicate to upper layers that max retransmission has been reached” (See section 5.2.1)
  • “SDU discard procedures” (See section 5.3)
  • “Re-establishment procedure” (See section 5.4)

We do not support any of the additional primitives of RLC SAP for AM RLC entity. In particular:

  • no SDU discard notified by PDCP
  • no notification of successful / failed delivery by AM RLC entity to PDCP entity
UM RLC

In this section we describe the implemnetation of the Unacknowledge Mode (UM) RLC entity.

Transmit operations in downlink

The transmit operations of the UM RLC are similar to those of the AM RLC previously described in Section Transmit operations in downlink, with the difference that, following the specifications of [TS36322], retransmission are not performed, and there are no STATUS PDUs.

Transmit operations in uplink

The transmit operations in the uplink are similar to those of the downlink, with the main difference that the Report_Buffer_Status is sent from the UE MAC to the MAC Scheduler in the eNB over the air using the control channel.

Calculation of the buffer size

The calculation of the buffer size for the UM RLC is done using the same approach of the AM RLC, please refer to section Calculation of the buffer size for the corresponding description.

TM RLC

In this section we describe the implementation of the Transparent Mode (TM) RLC entity.

Transmit operations in downlink

In the simulator, the TM RLC still provides to the upper layers the same service interface provided by the AM and UM RLC entities to the PDCP layer; in practice, this interface is used by an RRC entity (not a PDCP entity) for the transmission of RLC SDUs. This choice is motivated by the fact that the services provided by the TM RLC to the upper layers, according to [TS36322], is a subset of those provided by the UM and AM RLC entities to the PDCP layer; hence, we reused the same interface for simplicity.

The transmit operations in the downlink are performed as follows. When the Transmit_PDCP_PDU service primitive is called by the upper layers, the TM RLC does the following:

  • put the SDU in the Transmission Buffer
  • compute the size of the Transmission Buffer
  • call the Report_Buffer_Status service primitive of the eNB MAC entity

Afterwards, when the MAC scheduler decides that some data can be sent by the logical channel to which the TM RLC entity belongs, the MAC entity notifies it to the TM RLC entity by calling the Notify_Tx_Opportunity service primitive. Upon reception of this primitive, the TM RLC entity does the following:

  • if the TX opportunity has a size that is greater than or equal to the size of the head-of-line SDU in the Transmission Buffer
    • dequeue the head-of-line SDU from the Transmission Buffer
    • create one RLC PDU that contains entirely that SDU, without any RLC header
    • Call the Transmit_PDU primitive in order to send the RLC PDU to the MAC entity.
Transmit operations in uplink

The transmit operations in the uplink are similar to those of the downlink, with the main difference that a transmission opportunity can also arise from the assignment of the UL GRANT as part of the Random Access procedure, without an explicit Buffer Status Report issued by the TM RLC entity.

Calculation of the buffer size

As per the specifications [TS36322], the TM RLC does not add any RLC header to the PDUs being transmitted. Because of this, the buffer size reported to the MAC layer is calculated simply by summing the size of all packets in the transmission buffer, thus notifying to the MAC the exact buffer size.

SM RLC

In addition to the AM, UM and TM implementations that are modeled after the 3GPP specifications, a simplified RLC model is provided, which is called Saturation Mode (SM) RLC. This RLC model does not accept PDUs from any above layer (such as PDCP); rather, the SM RLC takes care of the generation of RLC PDUs in response to the notification of transmission opportunities notified by the MAC. In other words, the SM RLC simulates saturation conditions, i.e., it assumes that the RLC buffer is always full and can generate a new PDU whenever notified by the scheduler.

The SM RLC is used for simplified simulation scenarios in which only the LTE Radio model is used, without the EPC and hence without any IP networking support. We note that, although the SM RLC is an unrealistic traffic model, it still allows for the correct simulation of scenarios with multiple flows belonging to different (non real-time) QoS classes, in order to test the QoS performance obtained by different schedulers. This can be done since it is the task of the Scheduler to assign transmission resources based on the characteristics (e.g., Guaranteed Bit Rate) of each Radio Bearer, which are specified upon the definition of each Bearer within the simulation program.

As for schedulers designed to work with real-time QoS traffic that has delay constraints, the SM RLC is probably not an appropriate choice. This is because the absence of actual RLC SDUs (replaced by the artificial generation of Buffer Status Reports) makes it not possible to provide the Scheduler with meaningful head-of-line-delay information, which is often the metric of choice for the implementation of scheduling policies for real-time traffic flows. For the simulation and testing of such schedulers, it is advisable to use either the UM or the AM RLC models instead.

PDCP

PDCP Model Overview

The reference document for the specification of the PDCP entity is [TS36323]. With respect to this specification, the PDCP model implemented in the simulator supports only the following features:

  • transfer of data (user plane or control plane);
  • maintenance of PDCP SNs;
  • transfer of SN status (for use upon handover);

The following features are currently not supported:

  • header compression and decompression of IP data flows using the ROHC protocol;
  • in-sequence delivery of upper layer PDUs at re-establishment of lower layers;
  • duplicate elimination of lower layer SDUs at re-establishment of lower layers for radio bearers mapped on RLC AM;
  • ciphering and deciphering of user plane data and control plane data;
  • integrity protection and integrity verification of control plane data;
  • timer based discard;
  • duplicate discarding.
PDCP Service Interface

The PDCP service interface is divided into two parts:

  • the PdcpSapProvider part is provided by the PDCP layer and used by the upper layer and
  • the PdcpSapUser part is provided by the upper layer and used by the PDCP layer.
PDCP Service Primitives

The following list specifies which service primitives are provided by the PDCP service interfaces:

  • PdcpSapProvider::TransmitPdcpSdu

    • The RRC entity uses this primitive to send an RRC PDU to the lower PDCP entity in the transmitter peer
  • PdcpSapUser::ReceivePdcpSdu

    • The PDCP entity uses this primitive to send an RRC PDU to the upper RRC entity in the receiver peer

RRC

Features

The RRC model implemented in the simulator provides the following functionality:

  • generation (at the eNB) and interpretation (at the UE) of System Information (in particular the Master Information Block and, at the time of this writing, only System Information Block Type 1 and 2)
  • initial cell selection
  • RRC connection establishment procedure
  • RRC reconfiguration procedure, supporting the following use cases: + reconfiguration of the SRS configuration index + reconfiguration of the PHY TX mode (MIMO) + reconfiguration of UE measurements + data radio bearer setup + handover
  • RRC connection re-establishment, supporting the following use cases: + handover
Architecture

The RRC model is divided into the following components:

  • the RRC entities LteUeRrc and LteEnbRrc, which implement the state machines of the RRC entities respectively at the UE and the eNB;
  • the RRC SAPs LteUeRrcSapProvider, LteUeRrcSapUser, LteEnbRrcSapProvider, LteEnbRrcSapUser, which allow the RRC entities to send and receive RRC messages and information elmenents;
  • the RRC protocol classes LteUeRrcProtocolIdeal, LteEnbRrcProtocolIdeal, LteUeRrcProtocolReal, LteEnbRrcProtocolReal, which implement two different models for the transmission of RRC messages.

Additionally, the RRC components use various other SAPs in order to interact with the rest of the protocol stack. A representation of all the SAPs that are used is provided in the figures LTE radio protocol stack architecture for the UE on the data plane, LTE radio protocol stack architecture for the UE on the control plane, LTE radio protocol stack architecture for the eNB on the data plane and LTE radio protocol stack architecture for the eNB on the control plane.

UE RRC State Machine

In Figure UE RRC State Machine we represent the state machine as implemented in the RRC UE entity.

_images/lte-ue-rrc-states.png

UE RRC State Machine

It is to be noted that most of the states are transient, i.e., once the UE goes into one of the CONNECTED states it will never switch back to any of the IDLE states. This choice is done for the following reasons:

  • as discussed in the section Design Criteria, the focus of the LTE-EPC simulation model is on CONNECTED mode
  • radio link failure is not currently modeled, as discussed in the section Radio Link Failure, so an UE cannot go IDLE because of radio link failure
  • RRC connection release is currently never triggered neither by the EPC nor by the NAS

Still, we chose to model explicitly the IDLE states, because:

  • a realistic UE RRC configuration is needed for handover, which is a required feature, and in order to have a cleaner implementation it makes sense to use the same UE RRC configuration also for the initial connection establishment
  • it makes easier to implement idle mode cell selection in the future, which is a highly desirable feature
ENB RRC State Machine

The eNB RRC maintains the state for each UE that is attached to the cell. From an implementation point of view, the state of each UE is contained in an instance of the UeManager class. The state machine is represented in Figure ENB RRC State Machine for each UE.

_images/lte-enb-rrc-states.png

ENB RRC State Machine for each UE

Initial Cell Selection

Initial cell selection is an IDLE mode procedure, performed by UE when it has not yet camped or attached to an eNodeB. The objective of the procedure is to find a suitable cell and attach to it to gain access to the cellular network.

It is typically done at the beginning of simulation, as depicted in Figure Sample runs of initial cell selection in UE and timing of related events below. The time diagram on the left side is illustrating the case where initial cell selection succeed on first try, while the diagram on the right side is for the case where it fails on the first try and succeed on the second try. The timing assumes the use of real RRC protocol model (see RRC protocol models) and no transmission error.

_images/lte-cell-selection-timeline.png

Sample runs of initial cell selection in UE and timing of related events

The functionality is based on 3GPP IDLE mode specifications, such as in [TS36300], [TS36304], and [TS36331]. However, a proper implementation of IDLE mode is still missing in the simulator, so we reserve several simplifying assumptions:

  • multiple carrier frequency is not supported;
  • multiple Public Land Mobile Network (PLMN) identities (i.e. multiple network operators) is not supported;
  • RSRQ measurements are not utilized;
  • stored information cell selection is not supported;
  • “Any Cell Selection” state and camping to an acceptable cell is not supported;
  • marking a cell as barred or reserved is not supported;
  • cell reselection is not supported, hence it is not possible for UE to camp to a different cell after the initial camp has been placed; and
  • UE’s Closed Subscriber Group (CSG) white list contains only one CSG identity.

Also note that initial cell selection is only available for EPC-enabled simulations. LTE-only simulations must use the manual attachment method. See section Network Attachment of the User Documentation for more information on their differences in usage.

The next subsections cover different parts of initial cell selection, namely cell search, broadcast of system information, and cell selection evaluation.

Broadcast of System Information

System information blocks are broadcasted by eNodeB to UEs at predefined time intervals, adapted from Section 5.2.1.2 of [TS36331]. The supported system information blocks are:

  • Master Information Block (MIB)

    Contains parameters related to the PHY layer, generated during cell configuration and broadcasted every 10 ms at the beginning of radio frame as a control message.

  • System Information Block Type 1 (SIB1)

    Contains information regarding network access, broadcasted every 20 ms at the middle of radio frame as a control message. Not used in manual attachment method. UE must have decoded MIB before it can receive SIB1.

  • System Information Block Type 2 (SIB2)

    Contains UL- and RACH-related settings, scheduled to transmit via RRC protocol at 16 ms after cell configuration, and then repeats every 80 ms (configurable through LteEnbRrc::SystemInformationPeriodicity attribute. UE must be camped to a cell in order to be able to receive its SIB2.

Reception of system information is fundamental for UE to advance in its lifecycle. MIB enables the UE to increase the initial DL bandwidth of 6 RBs to the actual operating bandwidth of the network. SIB1 provides information necessary for cell selection evaluation (explained in the next section). And finally SIB2 is required before the UE is allowed to switch to CONNECTED state.

Cell Selection Evaluation

UE RRC reviews the measurement report produced in Cell Search and the cell access information provided by SIB1. Once both information is available for a specific cell, the UE triggers the evaluation process. The purpose of this process is to determine whether the cell is a suitable cell to camp to.

The evaluation process is a slightly simplified version of Section 5.2.3.2 of [TS36304]. It consists of the following criteria:

  • Rx level criterion; and
  • closed subscriber group (CSG) criterion.

The first criterion, Rx level, is based on the cell’s measured RSRP Q_{rxlevmeas}, which has to be higher than a required minimum Q_{rxlevmin} in order to pass the criterion:

Q_{rxlevmeas} - Q_{rxlevmin} > 0

where Q_{rxlevmin} is determined by each eNodeB and is obtainable by UE from SIB1.

The last criterion, CSG, is a combination of a true-or-false parameter called CSG indication and a simple number CSG identity. The basic rule is that UE shall not camp to eNodeB with a different CSG identity. But this rule is only enforced when CSG indication is valued as true. More details are provided in Section Network Attachment of the User Documentation.

When the cell passes all the above criteria, the cell is deemed as suitable. Then UE camps to it (IDLE_CAMPED_NORMALLY state).

After this, upper layer may request UE to enter CONNECTED mode. Please refer to section RRC connection establishment for details on this.

On the other hand, when the cell does not pass the CSG criterion, then the cell is labeled as acceptable (Section 10.1.1.1 [TS36300]). In this case, the RRC entity will tell the PHY entity to synchronize to the second strongest cell and repeat the initial cell selection procedure using that cell. As long as no suitable cell is found, the UE will repeat these steps while avoiding cells that have been identified as acceptable.

Radio Admission Control

Radio Admission Control is supported by having the eNB RRC reply to an RRC CONNECTION REQUEST message sent by the UE with either an RRC CONNECTION SETUP message or an RRC CONNECTION REJECT message, depending on whether the new UE is to be admitted or not. In the current implementation, the behavior is determined by the boolean attribute ns3::LteEnbRrc::AdmitRrcConnectionRequest. There is currently no Radio Admission Control algorithm that dynamically decides whether a new connection shall be admitted or not.

Radio Bearer Configuration

Some implementation choices have been made in the RRC regarding the setup of radio bearers:

  • three Logical Channel Groups (out of four available) are configured for uplink buffer status report purposes, according to the following policy:
    • LCG 0 is for signaling radio bearers
    • LCG 1 is for GBR data radio bearers
    • LCG 2 is for Non-GBR data radio bearers
UE RRC Measurements Model
UE RRC measurements support

The UE RRC entity provides support for UE measurements; in particular, it implements the procedures described in Section 5.5 of [TS36331], with the following simplifying assumptions:

  • only E-UTRA intra-frequency measurements are supported, which implies:
    • only one measurement object is used during the simulation;
    • measurement gaps are not needed to perform the measurements;
    • Event B1 and B2 are not implemented;
  • only reportStrongestCells purpose is supported, while reportCGI and reportStrongestCellsForSON purposes are not supported;
  • s-Measure is not supported;
  • since carrier aggregation is not supported in by the LTE module, the following assumptions in UE measurements hold true:
    • no notion of secondary cell (SCell);
    • primary cell (PCell) simply means serving cell;
    • Event A6 is not implemented;
  • speed dependant scaling of time-to-trigger (Section 5.5.6.2 of [TS36331]) is not supported.
Overall design

The model is based on the concept of UE measurements consumer, which is an entity that may request an eNodeB RRC entity to provide UE measurement reports. Consumers are, for example, Handover algorithm, which compute handover decision based on UE measurement reports. Test cases and user’s programs may also become consumers. Figure Relationship between UE measurements and its consumers depicts the relationship between these entities.

_images/ue-meas-consumer.png

Relationship between UE measurements and its consumers

The whole UE measurements function at the RRC level is divided into 4 major parts:

  1. Measurement configuration (handled by LteUeRrc::ApplyMeasConfig)
  2. Performing measurements (handled by LteUeRrc::DoReportUeMeasurements)
  3. Measurement report triggering (handled by LteUeRrc::MeasurementReportTriggering)
  4. Measurement reporting (handled by LteUeRrc::SendMeasurementReport)

The following sections will describe each of the parts above.

Measurement configuration

An eNodeB RRC entity configures UE measurements by sending the configuration parameters to the UE RRC entity. This set of parameters are defined within the MeasConfig Information Element (IE) of the RRC Connection Reconfiguration message (RRC connection reconfiguration).

The eNodeB RRC entity implements the configuration parameters and procedures described in Section 5.5.2 of [TS36331], with the following simplifying assumption:

  • configuration (i.e. addition, modification, and removal) can only be done before the simulation begins;
  • all UEs attached to the eNodeB will be configured the same way, i.e. there is no support for configuring specific measurement for specific UE; and
  • it is assumed that there is a one-to-one mapping between the PCI and the E-UTRAN Global Cell Identifier (EGCI). This is consistent with the PCI modeling assumptions described in UE PHY Measurements Model.

The eNodeB RRC instance here acts as an intermediary between the consumers and the attached UEs. At the beginning of simulation, each consumer provides the eNodeB RRC instance with the UE measurements configuration that it requires. After that, the eNodeB RRC distributes the configuration to attached UEs.

Users may customize the measurement configuration using several methods. Please refer to Section Configure UE measurements of the User Documentation for the description of these methods.

Performing measurements

UE RRC receives both RSRP and RSRQ measurements on periodical basis from UE PHY, as described in UE PHY Measurements Model. Layer 3 filtering will be applied to these received measurements. The implementation of the filtering follows Section 5.5.3.2 of [TS36331]:

F_n = (1 - a) \times F_{n-1} + a \times M_n

where:

  • M_n is the latest received measurement result from the physical layer;
  • F_n is the updated filtered measurement result;
  • F_{n-1} is the old filtered measurement result, where F_0 = M_1 (i.e. the first measurement is not filtered); and
  • a = (\frac{1}{2})^{\frac{k}{4}}, where k is the configurable filterCoefficent provided by the QuantityConfig;

k = 4 is the default value, but can be configured by setting the RsrpFilterCoefficient and RsrqFilterCoefficient attributes in LteEnbRrc.

Therefore k = 0 will disable Layer 3 filtering. On the other hand, past measurements can be granted more influence on the filtering results by using larger value of k.

Measurement reporting triggering

In this part, UE RRC will go through the list of active measurement configuration and check whether the triggering condition is fulfilled in accordance with Section 5.5.4 of [TS36331]. When at least one triggering condition from all the active measurement configuration is fulfilled, the measurement reporting procedure (described in the next subsection) will be initiated.

3GPP defines two kinds of triggerType: periodical and event-based. At the moment, only event-based criterion is supported. There are various events that can be selected, which are briefly described in the table below:

List of supported event-based triggering criteria
Name Description
Event A1 Serving cell becomes better than threshold
Event A2 Serving cell becomes worse than threshold
Event A3 Neighbour becomes offset dB better than serving cell
Event A4 Neighbour becomes better than threshold
Event A5 Serving becomes worse than threshold1 AND neighbour becomes better than threshold2

Two main conditions to be checked in an event-based trigger are the entering condition and the leaving condition. More details on these two can be found in Section 5.5.4 of [TS36331].

An event-based trigger can be further configured by introducing hysteresis and time-to-trigger. Hysteresis (Hys) defines the distance between the entering and leaving conditions in dB. Similarly, time-to-trigger introduces delay to both entering and leaving conditions, but as a unit of time.

The periodical type of reporting trigger is not supported, but its behaviour can be easily obtained by using an event-based trigger. This can be done by configuring the measurement in such a way that the entering condition is always fulfilled, for example, by setting the threshold of Event A1 to zero (the minimum level). As a result, the measurement reports will always be triggered at every certain interval, as determined by the reportInterval field within LteRrcSap::ReportConfigEutra, therefore producing the same behaviour as periodical reporting.

As a limitation with respect to 3GPP specifications, the current model does not support any cell-specific configuration. These configuration parameters are defined in measurement object. As a consequence, incorporating a list of black cells into the triggering process is not supported. Moreover, cell-specific offset (i.e., O_{cn} and O_{cp} in Event A3, A4, and A5) are not supported as well. The value equal to zero is always assumed in place of them.

Measurement reporting

This part handles the submission of measurement report from the UE RRC entity to the serving eNodeB entity via RRC protocol. Several simplifying assumptions have been adopted:

  • reportAmount is not applicable (i.e. always assumed to be infinite);
  • in measurement reports, the reportQuantity is always assumed to be BOTH, i.e., both RSRP and RSRQ are always reported, regardless of the triggerQuantity.
Handover

The RRC model supports UE mobility in CONNECTED mode by invoking the X2-based handover procedure. The model is intra-EUTRAN and intra-frequency, as based on Section 10.1.2.1 of [TS36300].

This section focuses on the process of triggering a handover. The handover execution procedure itself is covered in Section X2.

There are two ways to trigger the handover procedure:

  • explicitly (or manually) triggered by the simulation program by scheduling an execution of the method LteEnbRrc::SendHandoverRequest; or
  • automatically triggered by the eNodeB RRC entity based on UE measurements and according to the selected handover algorithm.

Section X2-based handover of the User Documentation provides some examples on using both explicit and automatic handover triggers in simulation. The next subsection will take a closer look on the automatic method, by describing the design aspects of the handover algorithm interface and the available handover algorithms.

Handover algorithm

Handover in 3GPP LTE has the following properties:

  • UE-assisted

    The UE provides input to the network in the form of measurement reports. This is handled by the UE RRC Measurements Model.

  • Network-controlled

    The network (i.e. the source eNodeB and the target eNodeB) decides when to trigger the handover and oversees its execution.

The handover algorithm operates at the source eNodeB and is responsible in making handover decisions in an “automatic” manner. It interacts with an eNodeB RRC instance via the Handover Management SAP interface. These relationships are illustrated in Figure Relationship between UE measurements and its consumers from the previous section.

The handover algorithm interface consists of the following methods:

  • AddUeMeasReportConfigForHandover

    (Handover Algorithm -> eNodeB RRC) Used by the handover algorithm to request measurement reports from the eNodeB RRC entity, by passing the desired reporting configuration. The configuration will be applied to all future attached UEs.

  • ReportUeMeas

    (eNodeB RRC -> Handover Algorithm) Based on the UE measurements configured earlier in AddUeMeasReportConfigForHandover, UE may submit measurement reports to the eNodeB. The eNodeB RRC entity uses the ReportUeMeas interface to forward these measurement reports to the handover algorithm.

  • TriggerHandover

    (Handover Algorithm -> eNodeB RRC) After examining the measurement reports (but not necessarily), the handover algorithm may declare a handover. This method is used to notify the eNodeB RRC entity about this decision, which will then proceed to commence the handover procedure.

One note for the AddUeMeasReportConfigForHandover. The method will return the measId (measurement identity) of the newly created measurement configuration. Typically a handover algorithm would store this unique number. It may be useful in the ReportUeMeas method, for example when more than one configuration has been requested and the handover algorithm needs to differentiate incoming reports based on the configuration that triggered them.

A handover algorithm is implemented by writing a subclass of the LteHandoverAlgorithm abstract superclass and implementing each of the above mentioned SAP interface methods. Users may develop their own handover algorithm this way, and then use it in any simulation by following the steps outlined in Section X2-based handover of the User Documentation.

Alternatively, users may choose to use one of the 3 built-in handover algorithms provided by the LTE module: no-op, A2-A4-RSRQ, and strongest cell handover algorithm. They are ready to be used in simulations or can be taken as an example of implementing a handover algorithm. Each of these built-in algorithms is covered in each of the following subsections.

No-op handover algorithm

The no-op handover algorithm (NoOpHandoverAlgorithm class) is the simplest possible implementation of handover algorithm. It basically does nothing, i.e., does not call any of the Handover Management SAP interface methods. Users may choose this handover algorithm if they wish to disable automatic handover trigger in their simulation.

A2-A4-RSRQ handover algorithm

The A2-A4-RSRQ handover algorithm provides the functionality of the default handover algorithm originally included in LENA M6 (ns-3.18), ported to the Handover Management SAP interface as the A2A4RsrqHandoverAlgorithm class.

As the name implies, the algorithm utilizes the Reference Signal Received Quality (RSRQ) measurements acquired from Event A2 and Event A4. Thus, the algorithm will add 2 measurement configuration to the corresponding eNodeB RRC instance. Their intended use are described as follows:

  • Event A2 (serving cell’s RSRQ becomes worse than threshold) is leveraged to indicate that the UE is experiencing poor signal quality and may benefit from a handover.
  • Event A4 (neighbour cell’s RSRQ becomes better than threshold) is used to detect neighbouring cells and acquire their corresponding RSRQ from every attached UE, which are then stored internally by the algorithm. By default, the algorithm configures Event A4 with a very low threshold, so that the trigger criteria are always true.

Figure A2-A4-RSRQ handover algorithm below summarizes this procedure.

_images/lte-legacy-handover-algorithm.png

A2-A4-RSRQ handover algorithm

Two attributes can be set to tune the algorithm behaviour:

  • ServingCellThreshold

    The threshold for Event A2, i.e. a UE must have an RSRQ lower than this threshold to be considered for a handover.

  • NeighbourCellOffset

    The offset that aims to ensure that the UE would receive better signal quality after the handover. A neighbouring cell is considered as a target cell for the handover only if its RSRQ is higher than the serving cell’s RSRQ by the amount of this offset.

The value of both attributes are expressed as RSRQ range (Section 9.1.7 of [TS36133]), which is an integer between 0 and 34, with 0 as the lowest RSRQ.

Strongest cell handover algorithm

The strongest cell handover algorithm, or also sometimes known as the traditional power budget (PBGT) algorithm, is developed using [Dimou2009] as reference. The idea is to provide each UE with the best possible Reference Signal Received Power (RSRP). This is done by performing a handover as soon as a better cell (i.e. with stronger RSRP) is detected.

Event A3 (neighbour cell’s RSRP becomes better than serving cell’s RSRP) is chosen to realize this concept. The A3RsrpHandoverAlgorithm class is the result of the implementation. Handover is triggered for the UE to the best cell in the measurement report.

A simulation which uses this algorithm is usually more vulnerable to ping-pong handover (consecutive handover to the previous source eNodeB within short period of time), especially when the Fading Model is enabled. This problem is typically tackled by introducing a certain delay to the handover. The algorithm does this by including hysteresis and time-to-trigger parameters (Section 6.3.5 of [TS36331]) to the UE measurements configuration.

Hysteresis (a.k.a. handover margin) delays the handover in regard of RSRP. The value is expressed in dB, ranges between 0 to 15 dB, and have a 0.5 dB accuracy, e.g., an input value of 2.7 dB is rounded to 2.5 dB.

On the other hand, time-to-trigger delays the handover in regard of time. 3GPP defines 16 valid values for time-to-trigger (all in milliseconds): 0, 40, 64, 80, 100, 128, 160, 256, 320, 480, 512, 640, 1024, 1280, 2560, and 5120.

The difference between hysteresis and time-to-trigger is illustrated in Figure Effect of hysteresis and time-to-trigger in strongest cell handover algorithm below, which is taken from the lena-x2-handover-measures example. It depicts the perceived RSRP of serving cell and a neighbouring cell by a UE which moves pass the border of the cells.

_images/lte-strongest-cell-handover-algorithm.png

Effect of hysteresis and time-to-trigger in strongest cell handover algorithm

By default, the algorithm uses a hysteresis of 3.0 dB and time-to-trigger of 256 ms. These values can be tuned through the Hysteresis and TimeToTrigger attributes of the A3RsrpHandoverAlgorithm class.

Neighbour Relation

LTE module supports a simplified Automatic Neighbour Relation (ANR) function. This is handled by the LteAnr class, which interacts with an eNodeB RRC instance through the ANR SAP interface.

Neighbour Relation Table

The ANR holds a Neighbour Relation Table (NRT), similar to the description in Section 22.3.2a of [TS36300]. Each entry in the table is called a Neighbour Relation (NR) and represents a detected neighbouring cell, which contains the following boolean fields:

  • No Remove

    Indicates that the NR shall not be removed from the NRT. This is true by default for user-provided NR and false otherwise.

  • No X2

    Indicates that the NR shall not use an X2 interface in order to initiate procedures towards the eNodeB parenting the target cell. This is false by default for user-provided NR, and true otherwise.

  • No HO

    Indicates that the NR shall not be used by the eNodeB for handover reasons. This is true in most cases, except when the NR is both user-provided and network-detected.

Each NR entry may have at least one of the following properties:

  • User-provided

    This type of NR is created as instructed by the simulation user. For example, a NR is created automatically upon a user-initiated establishment of X2 connection between 2 eNodeBs, e.g. as described in Section X2-based handover. Another way to create a user-provided NR is to call the AddNeighbourRelation function explicitly.

  • Network-detected

    This type of NR is automatically created during the simulation as a result of the discovery of a nearby cell.

In order to automatically create network-detected NR, ANR utilizes UE measurements. In other words, ANR is a consumer of UE measurements, as depicted in Figure Relationship between UE measurements and its consumers. RSRQ and Event A4 (neighbour becomes better than threshold) are used for the reporting configuration. The default Event A4 threshold is set to the lowest possible, i.e., maximum detection capability, but can be changed by setting the Threshold attribute of LteAnr class. Note that the A2-A4-RSRQ handover algorithm also utilizes a similar reporting configuration. Despite the similarity, when both ANR and this handover algorithm are active in the eNodeB, they use separate reporting configuration.

Also note that automatic setup of X2 interface is not supported. This is the reason why the No X2 and No HO fields are true in a network-detected but not user-detected NR.

Role of ANR in Simulation

The ANR SAP interface provides the means of communication between ANR and eNodeB RRC. Some interface functions are used by eNodeB RRC to interact with the NRT, as shown below:

  • AddNeighbourRelation

    (eNodeB RRC -> ANR) Add a new user-provided NR entry into the NRT.

  • GetNoRemove

    (eNodeB RRC -> ANR) Get the value of No Remove field of an NR entry of the given cell ID.

  • GetNoHo

    (eNodeB RRC -> ANR) Get the value of No HO field of an NR entry of the given cell ID.

  • GetNoX2

    (eNodeB RRC -> ANR) Get the value of No X2 field of an NR entry of the given cell ID.

Other interface functions exist to support the role of ANR as a UE measurements consumer, as listed below:

  • AddUeMeasReportConfigForAnr

    (ANR -> eNodeB RRC) Used by the ANR to request measurement reports from the eNodeB RRC entity, by passing the desired reporting configuration. The configuration will be applied to all future attached UEs.

  • ReportUeMeas

    (eNodeB RRC -> ANR) Based on the UE measurements configured earlier in AddUeMeasReportConfigForAnr, UE may submit measurement reports to the eNodeB. The eNodeB RRC entity uses the ReportUeMeas interface to forward these measurement reports to the ANR.

Please refer to the corresponding API documentation for LteAnrSap class for more details on the usage and the required parameters.

The ANR is utilized by the eNodeB RRC instance as a data structure to keep track of the situation of nearby neighbouring cells. The ANR also helps the eNodeB RRC instance to determine whether it is possible to execute a handover procedure to a neighbouring cell. This is realized by the fact that eNodeB RRC will only allow a handover procedure to happen if the NR entry of the target cell has both No HO and No X2 fields set to false.

ANR is enabled by default in every eNodeB instance in the simulation. It can be disabled by setting the AnrEnabled attribute in LteHelper class to false.

RRC sequence diagrams

In this section we provide some sequence diagrams that explain the most important RRC procedures being modeled.

RRC connection establishment

Figure Sequence diagram of the RRC Connection Establishment procedure shows how the RRC Connection Establishment procedure is modeled, highlighting the role of the RRC layer at both the UE and the eNB, as well as the interaction with the other layers.

_images/rrc-connection-establishment.png

Sequence diagram of the RRC Connection Establishment procedure

RRC connection reconfiguration

Figure Sequence diagram of the RRC Connection Reconfiguration procedure shows how the RRC Connection Reconfiguration procedure is modeled for the case where MobilityControlInfo is not provided, i.e., handover is not performed.

_images/rrc-connection-reconfiguration.png

Sequence diagram of the RRC Connection Reconfiguration procedure

Figure Sequence diagram of the RRC Connection Reconfiguration procedure for the handover case shows how the RRC Connection Reconfiguration procedure is modeled for the case where MobilityControlInfo is provided, i.e., handover is to be performed. As specified in [TS36331], After receiving the handover message, the UE attempts to access the target cell at the first available RACH occasion according to Random Access resource selection defined in [TS36321]_, i.e. the handover is asynchronous. Consequently, when allocating a dedicated preamble for the random access in the target cell, E-UTRA shall ensure it is available from the first RACH occasion the UE may use. Upon successful completion of the handover, the UE sends a message used to confirm the handover. Note that the random access procedure in this case is non-contention based, hence in a real LTE system it differs slightly from the one used in RRC connection established. Also note that the RA Preamble ID is signalled via the Handover Command included in the X2 Handover Request ACK message sent from the target eNB to the source eNB; in particular, the preamble is included in the RACH-ConfigDedicated IE which is part of MobilityControlInfo.

_images/rrc-connection-reconfiguration-handover.png

Sequence diagram of the RRC Connection Reconfiguration procedure for the handover case

RRC protocol models

As previously anticipated, we provide two different models for the transmission and reception of RRC messages: Ideal and Real. Each of them is described in one of the following subsections.

Ideal RRC protocol model

According to this model, implemented in the classes and LteUeRrcProtocolIdeal and LteEnbRrcProtocolIdeal, all RRC messages and information elements are transmitted between the eNB and the UE in an ideal fashion, without consuming radio resources and without errors. From an implementation point of view, this is achieved by passing the RRC data structure directly between the UE and eNB RRC entities, without involving the lower layers (PDCP, RLC, MAC, scheduler).

Real RRC protocol model

This model is implemented in the classes LteUeRrcProtocolReal and LteEnbRrcProtocolReal and aims at modeling the transmission of RRC PDUs as commonly performed in real LTE systems. In particular:

  • for every RRC message being sent, a real RRC PDUs is created following the ASN.1 encoding of RRC PDUs and information elements (IEs) specified in [TS36331]. Some simplification are made with respect to the IEs included in the PDU, i.e., only those IEs that are useful for simulation purposes are included. For a detailed list, please see the IEs defined in lte-rrc-sap.h and compare with [TS36331].
  • the encoded RRC PDUs are sent on Signaling Radio Bearers and are subject to the same transmission modeling used for data communications, thus including scheduling, radio resource consumption, channel errors, delays, retransmissions, etc.
Signaling Radio Bearer model

We now describe the Signaling Radio Bearer model that is used for the Real RRC protocol model.

  • SRB0 messages (over CCCH):

    • RrcConnectionRequest: in real LTE systems, this is an RLC TM SDU sent over resources specified in the UL Grant in the RAR (not in UL DCIs); the reason is that C-RNTI is not known yet at this stage. In the simulator, this is modeled as a real RLC TM RLC PDU whose UL resources are allocated by the sched upon call to SCHED_DL_RACH_INFO_REQ.
    • RrcConnectionSetup: in the simulator this is implemented as in real LTE systems, i.e., with an RLC TM SDU sent over resources indicated by a regular UL DCI, allocated with SCHED_DL_RLC_BUFFER_REQ triggered by the RLC TM instance that is mapped to LCID 0 (the CCCH).
  • SRB1 messages (over DCCH):

    • All the SRB1 messages modeled in the simulator (e.g., RrcConnectionCompleted) are implemented as in real LTE systems, i.e., with a real RLC SDU sent over RLC AM using DL resources allocated via Buffer Status Reports. See the RLC model documentation for details.
  • SRB2 messages (over DCCH):

    • According to [TS36331], “SRB1 is for RRC messages (which may include a piggybacked NAS message) as well as for NAS messages prior to the establishment of SRB2, all using DCCH logical channel”, whereas “SRB2 is for NAS messages, using DCCH logical channel” and “SRB2 has a lower-priority than SRB1 and is always configured by E-UTRAN after security activation”. Modeling security-related aspects is not a requirement of the LTE simulation model, hence we always use SRB1 and never activate SRB2.
ASN.1 encoding of RRC IE’s

The messages defined in RRC SAP, common to all Ue/Enb SAP Users/Providers, are transported in a transparent container to/from a Ue/Enb. The encoding format for the different Information Elements are specified in [TS36331], using ASN.1 rules in the unaligned variant. The implementation in Ns3/Lte has been divided in the following classes:

  • Asn1Header : Contains the encoding / decoding of basic ASN types
  • RrcAsn1Header : Inherits Asn1Header and contains the encoding / decoding of common IE’s defined in [TS36331]
  • Rrc specific messages/IEs classes : A class for each of the messages defined in RRC SAP header
Asn1Header class - Implementation of base ASN.1 types

This class implements the methods to Serialize / Deserialize the ASN.1 types being used in [TS36331], according to the packed encoding rules in ITU-T X.691. The types considered are:

  • Boolean : a boolean value uses a single bit (1=true, 0=false).
  • Integer : a constrained integer (with min and max values defined) uses the minimum amount of bits to encode its range (max-min+1).
  • Bitstring : a bistring will be copied bit by bit to the serialization buffer.
  • Octetstring : not being currently used.
  • Sequence : the sequence generates a preamble indicating the presence of optional and default fields. It also adds a bit indicating the presence of extension marker.
  • Sequence...Of : the sequence...of type encodes the number of elements of the sequence as an integer (the subsequent elements will need to be encoded afterwards).
  • Choice : indicates which element among the ones in the choice set is being encoded.
  • Enumeration : is serialized as an integer indicating which value is used, among the ones in the enumeration, with the number of elements in the enumeration as upper bound.
  • Null : the null value is not encoded, although its serialization function is defined to provide a clearer map between specification and implementation.

The class inherits from ns-3 Header, but Deserialize() function is declared pure virtual, thus inherited classes having to implement it. The reason is that deserialization will retrieve the elements in RRC messages, each of them containing different information elements.

Additionally, it has to be noted that the resulting byte length of a specific type/message can vary, according to the presence of optional fields, and due to the optimized encoding. Hence, the serialized bits will be processed using PreSerialize() function, saving the result in m_serializationResult Buffer. As the methods to read/write in a ns3 buffer are defined in a byte basis, the serialization bits are stored into m_serializationPendingBits attribute, until the 8 bits are set and can be written to buffer iterator. Finally, when invoking Serialize(), the contents of the m_serializationResult attribute will be copied to Buffer::Iterator parameter

RrcAsn1Header : Common IEs

As some Information Elements are being used for several RRC messages, this class implements the following common IE’s:

  • SrbToAddModList
  • DrbToAddModList
  • LogicalChannelConfig
  • RadioResourceConfigDedicated
  • PhysicalConfigDedicated
  • SystemInformationBlockType1
  • SystemInformationBlockType2
  • RadioResourceConfigCommonSIB
Rrc specific messages/IEs classes

The following RRC SAP have been implemented:

  • RrcConnectionRequest
  • RrcConnectionSetup
  • RrcConnectionSetupCompleted
  • RrcConnectionReconfiguration
  • RrcConnectionReconfigurationCompleted
  • HandoverPreparationInfo
  • RrcConnectionReestablishmentRequest
  • RrcConnectionReestablishment
  • RrcConnectionReestablishmentComplete
  • RrcConnectionReestablishmentReject
  • RrcConnectionRelease

NAS

The focus of the LTE-EPC model is on the NAS Active state, which corresponds to EMM Registered, ECM connected, and RRC connected. Because of this, the following simplifications are made:

  • EMM and ECM are not modeled explicitly; instead, the NAS entity at the UE will interact directy with the MME to perfom actions that are equivalent (with gross simplifications) to taking the UE to the states EMM Connected and ECM Connected;
  • the NAS also takes care of multiplexing uplink data packets coming from the upper layers into the appropriate EPS bearer by using the Traffic Flow Template classifier (TftClassifier).
  • the NAS does not support PLMN and CSG selection
  • the NAS does not support any location update/paging procedure in idle mode

Figure Sequence diagram of the attach procedure shows how the simplified NAS model implements the attach procedure. Note that both the default and eventual dedicated EPS bearers are activated as part of this procedure.

_images/nas-attach.png

Sequence diagram of the attach procedure

S1

S1-U

The S1-U interface is modeled in a realistic way by encapsulating data packets over GTP/UDP/IP, as done in real LTE-EPC systems. The corresponding protocol stack is shown in Figure LTE-EPC data plane protocol stack. As shown in the figure, there are two different layers of IP networking. The first one is the end-to-end layer, which provides end-to-end connectivity to the users; this layers involves the UEs, the PGW and the remote host (including eventual internet routers and hosts in between), but does not involve the eNB. By default, UEs are assigned a public IPv4 address in the 7.0.0.0/8 network, and the PGW gets the address 7.0.0.1, which is used by all UEs as the gateway to reach the internet.

The second layer of IP networking is the EPC local area network. This involves all eNB nodes and the SGW/PGW node. This network is implemented as a set of point-to-point links which connect each eNB with the SGW/PGW node; thus, the SGW/PGW has a set of point-to-point devices, each providing connectivity to a different eNB. By default, a 10.x.y.z/30 subnet is assigned to each point-to-point link (a /30 subnet is the smallest subnet that allows for two distinct host addresses).

As specified by 3GPP, the end-to-end IP communications is tunneled over the local EPC IP network using GTP/UDP/IP. In the following, we explain how this tunneling is implemented in the EPC model. The explanation is done by discussing the end-to-end flow of data packets.

_images/epc-data-flow-dl.png

Data flow in the downlink between the internet and the UE

To begin with, we consider the case of the downlink, which is depicted in Figure Data flow in the downlink between the internet and the UE. Downlink Ipv4 packets are generated from a generic remote host, and addressed to one of the UE device. Internet routing will take care of forwarding the packet to the generic NetDevice of the SGW/PGW node which is connected to the internet (this is the Gi interface according to 3GPP terminology). The SGW/PGW has a VirtualNetDevice which is assigned the gateway IP address of the UE subnet; hence, static routing rules will cause the incoming packet from the internet to be routed through this VirtualNetDevice. Such device starts the GTP/UDP/IP tunneling procedure, by forwarding the packet to a dedicated application in the SGW/PGW node which is called EpcSgwPgwApplication. This application does the following operations:

  1. it determines the eNB node to which the UE is attached, by looking at the IP destination address (which is the address of the UE);
  2. it classifies the packet using Traffic Flow Templates (TFTs) to identify to which EPS Bearer it belongs. EPS bearers have a one-to-one mapping to S1-U Bearers, so this operation returns the GTP-U Tunnel Endpoint Identifier (TEID) to which the packet belongs;
  3. it adds the corresponding GTP-U protocol header to the packet;
  4. finally, it sends the packet over an UDP socket to the S1-U point-to-point NetDevice, addressed to the eNB to which the UE is attached.

As a consequence, the end-to-end IP packet with newly added IP, UDP and GTP headers is sent through one of the S1 links to the eNB, where it is received and delivered locally (as the destination address of the outmost IP header matches the eNB IP address). The local delivery process will forward the packet, via an UDP socket, to a dedicated application called EpcEnbApplication. This application then performs the following operations:

  1. it removes the GTP header and retrieves the TEID which is contained in it;
  2. leveraging on the one-to-one mapping between S1-U bearers and Radio Bearers (which is a 3GPP requirement), it determines the Bearer ID (BID) to which the packet belongs;
  3. it records the BID in a dedicated tag called EpsBearerTag, which is added to the packet;
  4. it forwards the packet to the LteEnbNetDevice of the eNB node via a raw packet socket

Note that, at this point, the outmost header of the packet is the end-to-end IP header, since the IP/UDP/GTP headers of the S1 protocol stack have already been stripped. Upon reception of the packet from the EpcEnbApplication, the LteEnbNetDevice will retrieve the BID from the EpsBearerTag, and based on the BID will determine the Radio Bearer instance (and the corresponding PDCP and RLC protocol instances) which are then used to forward the packet to the UE over the LTE radio interface. Finally, the LteUeNetDevice of the UE will receive the packet, and delivery it locally to the IP protocol stack, which will in turn delivery it to the application of the UE, which is the end point of the downlink communication.

_images/epc-data-flow-ul.png

Data flow in the uplink between the UE and the internet

The case of the uplink is depicted in Figure Data flow in the uplink between the UE and the internet. Uplink IP packets are generated by a generic application inside the UE, and forwarded by the local TCP/IP stack to the LteUeNetDevice of the UE. The LteUeNetDevice then performs the following operations:

  1. it classifies the packet using TFTs and determines the Radio Bearer to which the packet belongs (and the corresponding RBID);
  2. it identifies the corresponding PDCP protocol instance, which is the entry point of the LTE Radio Protocol stack for this packet;
  3. it sends the packet to the eNB over the LTE Radio Protocol stack.

The eNB receives the packet via its LteEnbNetDevice. Since there is a single PDCP and RLC protocol instance for each Radio Bearer, the LteEnbNetDevice is able to determine the BID of the packet. This BID is then recorded onto an EpsBearerTag, which is added to the packet. The LteEnbNetDevice then forwards the packet to the EpcEnbApplication via a raw packet socket.

Upon receiving the packet, the EpcEnbApplication performs the following operations:

  1. it retrieves the BID from the EpsBearerTag in the packet;
  2. it determines the corresponding EPS Bearer instance and GTP-U TEID by leveraging on the one-to-one mapping between S1-U bearers and Radio Bearers;
  3. it adds a GTP-U header on the packet, including the TEID determined previously;
  4. it sends the packet to the SGW/PGW node via the UDP socket connected to the S1-U point-to-point net device.

At this point, the packet contains the S1-U IP, UDP and GTP headers in addition to the original end-to-end IP header. When the packet is received by the corresponding S1-U point-to-point NetDevice of the SGW/PGW node, it is delivered locally (as the destination address of the outmost IP header matches the address of the point-to-point net device). The local delivery process will forward the packet to the EpcSgwPgwApplication via the correponding UDP socket. The EpcSgwPgwApplication then removes the GTP header and forwards the packet to the VirtualNetDevice. At this point, the outmost header of the packet is the end-to-end IP header. Hence, if the destination address within this header is a remote host on the internet, the packet is sent to the internet via the corresponding NetDevice of the SGW/PGW. In the event that the packet is addressed to another UE, the IP stack of the SGW/PGW will redirect the packet again to the VirtualNetDevice, and the packet will go through the dowlink delivery process in order to reach its destination UE.

Note that the EPS Bearer QoS is not enforced on the S1-U links, it is assumed that the overprovisioning of the link bandwidth is sufficient to meet the QoS requirements of all bearers.

S1AP

The S1-AP interface provides control plane interaction between the eNB and the MME. In the simulator, this interface is modeled in an ideal fashion, with direct interaction between the eNB and the MME objects, without actually implementing the encoding of S1AP messages and information elements specified in [TS36413] and without actually transmitting any PDU on any link.

The S1-AP primitives that are modeled are:

  • INITIAL UE MESSAGE
  • INITIAL CONTEXT SETUP REQUEST
  • INITIAL CONTEXT SETUP RESPONSE
  • PATH SWITCH REQUEST
  • PATH SWITCH REQUEST ACKNOWLEDGE

X2

The X2 interface interconnects two eNBs [TS36420]. From a logical point of view, the X2 interface is a point-to-point interface between the two eNBs. In a real E-UTRAN, the logical point-to-point interface should be feasible even in the absence of a physical direct connection between the two eNBs. In the X2 model implemented in the simulator, the X2 interface is a point-to-point link between the two eNBs. A point-to-point device is created in both eNBs and the two point-to-point devices are attached to the point-to-point link.

For a representation of how the X2 interface fits in the overall architecture of the LENA simulation model, the reader is referred to the figure Overview of the LTE-EPC simulation model.

The X2 interface implemented in the simulator provides detailed implementation of the following elementary procedures of the Mobility Management functionality [TS36423]:

  • Handover Request procedure
  • Handover Request Acknowledgement procedure
  • SN Status Transfer procedure
  • UE Context Release procedure

These procedures are involved in the X2-based handover. You can find the detailed description of the handover in section 10.1.2.1 of [TS36300]. We note that the simulator model currently supports only the seamless handover as defined in Section 2.6.3.1 of [Sesia2009]; in particular, lossless handover as described in Section 2.6.3.2 of [Sesia2009] is not supported at the time of this writing.

Figure Sequence diagram of the X2-based handover below shows the interaction of the entities of the X2 model in the simulator. The shaded labels indicate the moments when the UE or eNodeB transition to another RRC state.

_images/lte-epc-x2-handover-seq-diagram.png

Sequence diagram of the X2-based handover

The figure also shows two timers within the handover procedure: the handover leaving timer is maintained by the source eNodeB, while the handover joining timer by the target eNodeB. The duration of the timers can be configured in the HandoverLeavingTimeoutDuration and HandoverJoiningTimeoutDuration attributes of the respective LteEnbRrc instances. When one of these timers expire, the handover procedure is considered as failed.

However, there is no proper handling of handover failure in the current version of LTE module. Users should tune the simulation properly in order to avoid handover failure, otherwise unexpected behaviour may occur. Please refer to Section Tuning simulation with handover of the User Documentation for some tips regarding this matter.

The X2 model is an entity that uses services from:

  • the X2 interfaces,
    • They are implemented as Sockets on top of the point-to-point devices.
    • They are used to send/receive X2 messages through the X2-C and X2-U interfaces (i.e. the point-to-point device attached to the point-to-point link) towards the peer eNB.
  • the S1 application.
    • Currently, it is the EpcEnbApplication.
    • It is used to get some information needed for the Elementary Procedures of the X2 messages.

and it provides services to:

  • the RRC entity (X2 SAP)
    • to send/receive RRC messages. The X2 entity sends the RRC message as a transparent container in the X2 message. This RRC message is sent to the UE.

Figure Implementation Model of X2 entity and SAPs shows the implentation model of the X2 entity and its relationship with all the other entities and services in the protocol stack.

_images/lte-epc-x2-entity-saps.png

Implementation Model of X2 entity and SAPs

The RRC entity manages the initiation of the handover procedure. This is done in the Handover Management submodule of the eNB RRC entity. The target eNB may perform some Admission Control procedures. This is done in the Admission Control submodule. Initially, this submodule will accept any handover request.

X2 interfaces

The X2 model contains two interfaces:

  • the X2-C interface. It is the control interface and it is used to send the X2-AP PDUs (i.e. the elementary procedures).
  • the X2-U interface. It is used to send the bearer data when there is DL forwarding.

Figure X2 interface protocol stacks shows the protocol stacks of the X2-U interface and X2-C interface modeled in the simulator.

_images/lte-epc-x2-interface.png

X2 interface protocol stacks

X2-C

The X2-C interface is the control part of the X2 interface and it is used to send the X2-AP PDUs (i.e. the elementary procedures).

In the original X2 interface control plane protocol stack, SCTP is used as the transport protocol but currently, the SCTP protocol is not modeled in the ns-3 simulator and its implementation is out-of-scope of the project. The UDP protocol is used as the datagram oriented protocol instead of the SCTP protocol.

X2-U

The X2-U interface is used to send the bearer data when there is DL forwarding during the execution of the X2-based handover procedure. Similarly to what done for the S1-U interface, data packets are encapsulated over GTP/UDP/IP when being sent over this interface. Note that the EPS Bearer QoS is not enforced on the X2-U links, it is assumed that the overprovisioning of the link bandwidth is sufficient to meet the QoS requirements of all bearers.

X2 Service Interface

The X2 service interface is used by the RRC entity to send and receive messages of the X2 procedures. It is divided into two parts:

  • the EpcX2SapProvider part is provided by the X2 entity and used by the RRC entity and
  • the EpcX2SapUser part is provided by the RRC entity and used by the RRC enity.

The primitives that are supported in our X2-C model are described in the following subsections.

X2-C primitives for handover execution

The following primitives are used for the X2-based handover:

  • HANDOVER REQUEST
  • HANDOVER REQUEST ACK
  • HANDOVER PREPARATION FAILURE
  • SN STATUS STRANSFER
  • UE CONTEXT RELEASE

all the above primitives are used by the currently implemented RRC model during the preparation and execution of the handover procedure. Their usage interacts with the RRC state machine; therefore, they are not meant to be used for code customization, at least unless it is desired to modify the RRC state machine.

X2-C SON primitives

The following primitives can be used to implement Self-Organized Network (SON) functionalities:

  • LOAD INFORMATION
  • RESOURCE STATUS UPDATE

note that the current RRC model does not actually use these primitives, they are included in the model just to make it possible to develop SON algorithms included in the RRC logic that make use of them.

As a first example, we show here how the load information primitive can be used. We assume that the LteEnbRrc has been modified to include the following new member variables:

std::vector<EpcX2Sap::UlInterferenceOverloadIndicationItem>
  m_currentUlInterferenceOverloadIndicationList;
std::vector <EpcX2Sap::UlHighInterferenceInformationItem>
  m_currentUlHighInterferenceInformationList;
EpcX2Sap::RelativeNarrowbandTxBand m_currentRelativeNarrowbandTxBand;

for a detailed description of the type of these variables, we suggest to consult the file epc-x2-sap.h, the corresponding doxygen documentation, and the references therein to the relevant sections of 3GPP TS 36.423. Now, assume that at run time these variables have been set to meaningful values following the specifications just mentioned. Then, you can add the following code in the LteEnbRrc class implementation in order to send a load information primitive:

EpcX2Sap::CellInformationItem cii;
cii.sourceCellId = m_cellId;
cii.ulInterferenceOverloadIndicationList = m_currentUlInterferenceOverloadIndicationList;
cii.ulHighInterferenceInformationList = m_currentUlHighInterferenceInformationList;
cii.relativeNarrowbandTxBand = m_currentRelativeNarrowbandTxBand;

EpcX2Sap::LoadInformationParams params;
params.targetCellId = cellId;
params.cellInformationList.push_back (cii);
m_x2SapProvider->SendLoadInformation (params);

The above code allows the source eNB to send the message. The method LteEnbRrc::DoRecvLoadInformation will be called when the target eNB receives the message. The desired processing of the load information should therefore be implemented within that method.

In the following second example we show how the resource status update primitive is used. We assume that the LteEnbRrc has been modified to include the following new member variable:

EpcX2Sap::CellMeasurementResultItem m_cmri;

similarly to before, we refer to epc-x2-sap.h and the references therein for detailed information about this variable type. Again, we assume that the variable has been already set to a meaningful value. Then, you can add the following code in order to send a resource status update:

EpcX2Sap::ResourceStatusUpdateParams params;
params.targetCellId = cellId;
params.cellMeasurementResultList.push_back (m_cmri);
m_x2SapProvider->SendResourceStatusUpdate (params);

The method eEnbRrc::DoRecvResourceStatusUpdate will be called when the target eNB receives the resource status update message. The desired processing of this message should therefore be implemented within that method.

Finally, we note that the setting and processing of the appropriate values for the variable passed to the above described primitives is deemed to be specific of the SON algorithm being implemented, and hence is not covered by this documentation.

Unsupported primitives

Mobility Robustness Optimization primitives such as Radio Link Failure indication and Handover Report are not supported at this stage.

S11

The S11 interface provides control plane interaction between the SGW and the MME using the GTPv2-C protocol specified in [TS29274]. In the simulator, this interface is modeled in an ideal fashion, with direct interaction between the SGW and the MME objects, without actually implementing the encoding of the messages and without actually transmitting any PDU on any link.

The S11 primitives that are modeled are:

  • CREATE SESSION REQUEST
  • CREATE SESSION RESPONSE
  • MODIFY BEARER REQUEST
  • MODIFY BEARER RESPONSE

Of these primitives, the first two are used upon initial UE attachment for the establishment of the S1-U bearers; the other two are used during handover to switch the S1-U bearers from the source eNB to the target eNB as a consequence of the reception by the MME of a PATH SWITCH REQUEST S1-AP message.

Helpers

Two helper objects are use to setup simulations and configure the various components. These objects are:

  • LteHelper, which takes care of the configuration of the LTE radio access network, as well as of coordinating the setup and release of EPS bearers. The LteHelper class provides both the API definition and its implementation.
  • EpcHelper, which takes care of the configuration of the Evolved Packet Core. The EpcHelper class is an abstract base class which only provides the API definition; the implementation is delegated to child classes in order to allow for different EPC network models.

It is possible to create a simple LTE-only simulations by using LteHelper alone, or to create complete LTE-EPC simulations by using both LteHelper and EpcHelper. When both helpers are used, they interact in a master-slave fashion, with LteHelper being the Master that interacts directly with the user program, and EpcHelper working “under the hood” to configure the EPC upon explicit methods called by LteHelper. The exact interactions are displayed in the Figure Sequence diagram of the interaction between LteHelper and EpcHelper.

_images/helpers.png

Sequence diagram of the interaction between LteHelper and EpcHelper

User Documentation

Background

We assume the reader is already familiar with how to use the ns-3 simulator to run generic simulation programs. If this is not the case, we strongly recommend the reader to consult [ns3tutorial].

Usage Overview

The ns-3 LTE model is a software library that allows the simulation of LTE networks, optionally including the Evolved Packet Core (EPC). The process of performing such simulations typically involves the following steps:

  1. Define the scenario to be simulated
  2. Write a simulation program that recreates the desired scenario topology/architecture. This is done accessing the ns-3 LTE model library using the ns3::LteHelper API defined in src/lte/helper/lte-helper.h.
  3. Specify configuration parameters of the objects that are being used for the simulation. This can be done using input files (via the ns3::ConfigStore) or directly within the simulation program.
  4. Configure the desired output to be produced by the simulator
  5. Run the simulation.

All these aspects will be explained in the following sections by means of practical examples.

Basic simulation program

Here is the minimal simulation program that is needed to do an LTE-only simulation (without EPC).

  1. Initial boilerplate:

    #include <ns3/core-module.h>
    #include <ns3/network-module.h>
    #include <ns3/mobility-module.h>
    #include <ns3/lte-module.h>
    
    using namespace ns3;
    
    int main (int argc, char *argv[])
    {
      // the rest of the simulation program follows
    
  2. Create an LteHelper object:

    Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
    

    This will instantiate some common objects (e.g., the Channel object) and provide the methods to add eNBs and UEs and configure them.

  3. Create Node objects for the eNB(s) and the UEs:

    NodeContainer enbNodes;
    enbNodes.Create (1);
    NodeContainer ueNodes;
    ueNodes.Create (2);
    

    Note that the above Node instances at this point still don’t have an LTE protocol stack installed; they’re just empty nodes.

  4. Configure the Mobility model for all the nodes:

    MobilityHelper mobility;
    mobility.SetMobilityModel ("ns3::ConstantPositionMobilityModel");
    mobility.Install (enbNodes);
    mobility.SetMobilityModel ("ns3::ConstantPositionMobilityModel");
    mobility.Install (ueNodes);
    

    The above will place all nodes at the coordinates (0,0,0). Please refer to the documentation of the ns-3 mobility model for how to set your own position or configure node movement.

  5. Install an LTE protocol stack on the eNB(s):

    NetDeviceContainer enbDevs;
    enbDevs = lteHelper->InstallEnbDevice (enbNodes);
    
  6. Install an LTE protocol stack on the UEs:

    NetDeviceContainer ueDevs;
    ueDevs = lteHelper->InstallUeDevice (ueNodes);
    
  7. Attach the UEs to an eNB. This will configure each UE according to the eNB configuration, and create an RRC connection between them:

    lteHelper->Attach (ueDevs, enbDevs.Get (0));
    
  8. Activate a data radio bearer between each UE and the eNB it is attached to:

    enum EpsBearer::Qci q = EpsBearer::GBR_CONV_VOICE;
    EpsBearer bearer (q);
    lteHelper->ActivateDataRadioBearer (ueDevs, bearer);
    

    this method will also activate two saturation traffic generators for that bearer, one in uplink and one in downlink.

  9. Set the stop time:

    Simulator::Stop (Seconds (0.005));
    

    This is needed otherwise the simulation will last forever, because (among others) the start-of-subframe event is scheduled repeatedly, and the ns-3 simulator scheduler will hence never run out of events.

  10. Run the simulation:

    Simulator::Run ();
    
  11. Cleanup and exit:

    Simulator::Destroy ();
    return 0;
    }
    

For how to compile and run simulation programs, please refer to [ns3tutorial].

Configuration of LTE model parameters

All the relevant LTE model parameters are managed through the ns-3 attribute system. Please refer to the [ns3tutorial] and [ns3manual] for detailed information on all the possible methods to do it (environmental variables, C++ API, GtkConfigStore...).

In the following, we just briefly summarize how to do it using input files together with the ns-3 ConfigStore. First of all, you need to put the following in your simulation program, right after main () starts:

CommandLine cmd;
cmd.Parse (argc, argv);
ConfigStore inputConfig;
inputConfig.ConfigureDefaults ();
// parse again so you can override default values from the command line
cmd.Parse (argc, argv);

for the above to work, make sure you also #include "ns3/config-store.h". Now create a text file named (for example) input-defaults.txt specifying the new default values that you want to use for some attributes:

default ns3::LteHelper::Scheduler "ns3::PfFfMacScheduler"
default ns3::LteHelper::PathlossModel "ns3::FriisSpectrumPropagationLossModel"
default ns3::LteEnbNetDevice::UlBandwidth "25"
default ns3::LteEnbNetDevice::DlBandwidth "25"
default ns3::LteEnbNetDevice::DlEarfcn "100"
default ns3::LteEnbNetDevice::UlEarfcn "18100"
default ns3::LteUePhy::TxPower "10"
default ns3::LteUePhy::NoiseFigure "9"
default ns3::LteEnbPhy::TxPower "30"
default ns3::LteEnbPhy::NoiseFigure "5"

Supposing your simulation program is called src/lte/examples/lte-sim-with-input, you can now pass these settings to the simulation program in the following way:

./waf --command-template="%s --ns3::ConfigStore::Filename=input-defaults.txt --ns3::ConfigStore::Mode=Load --ns3::ConfigStore::FileFormat=RawText" --run src/lte/examples/lte-sim-with-input

Furthermore, you can generate a template input file with the following command:

./waf --command-template="%s --ns3::ConfigStore::Filename=input-defaults.txt --ns3::ConfigStore::Mode=Save --ns3::ConfigStore::FileFormat=RawText" --run src/lte/examples/lte-sim-with-input

note that the above will put in the file input-defaults.txt all the default values that are registered in your particular build of the simulator, including lots of non-LTE attributes.

Configure LTE MAC Scheduler

There are several types of LTE MAC scheduler user can choose here. User can use following codes to define scheduler type:

Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
lteHelper->SetSchedulerType ("ns3::FdMtFfMacScheduler");    // FD-MT scheduler
lteHelper->SetSchedulerType ("ns3::TdMtFfMacScheduler");    // TD-MT scheduler
lteHelper->SetSchedulerType ("ns3::TtaFfMacScheduler");     // TTA scheduler
lteHelper->SetSchedulerType ("ns3::FdBetFfMacScheduler");   // FD-BET scheduler
lteHelper->SetSchedulerType ("ns3::TdBetFfMacScheduler");   // TD-BET scheduler
lteHelper->SetSchedulerType ("ns3::FdTbfqFfMacScheduler");  // FD-TBFQ scheduler
lteHelper->SetSchedulerType ("ns3::TdTbfqFfMacScheduler");  // TD-TBFQ scheduler
lteHelper->SetSchedulerType ("ns3::PssFfMacScheduler");     //PSS scheduler

TBFQ and PSS have more parameters than other schedulers. Users can define those parameters in following way:

* TBFQ scheduler::

 Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
 lteHelper->SetSchedulerAttribute("DebtLimit", IntegerValue(yourvalue)); // default value -625000 bytes (-5Mb)
 lteHelper->SetSchedulerAttribute("CreditLimit", UintegerValue(yourvalue)); // default value 625000 bytes (5Mb)
 lteHelper->SetSchedulerAttribute("TokenPoolSize", UintegerValue(yourvalue)); // default value 1 byte
 lteHelper->SetSchedulerAttribute("CreditableThreshold", UintegerValue(yourvalue)); // default value 0

* PSS scheduler::

 Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
 lteHelper->SetSchedulerAttribute("nMux", UIntegerValue(yourvalue)); // the maximum number of UE selected by TD scheduler
 lteHelper->SetSchedulerAttribute("PssFdSchedulerType", StringValue("CoItA")); // PF scheduler type in PSS

In TBFQ, default values of debt limit and credit limit are set to -5Mb and 5Mb respectively based on paper [FABokhari2009]. Current implementation does not consider credit threshold (C = 0). In PSS, if user does not define nMux, PSS will set this value to half of total UE. The default FD scheduler is PFsch.

In addition, token generation rate in TBFQ and target bit rate in PSS need to be configured by Guarantee Bit Rate (GBR) or Maximum Bit Rate (MBR) in epc bearer QoS parameters. Users can use following codes to define GBR and MBR in both downlink and uplink:

Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
enum EpsBearer::Qci q = EpsBearer::yourvalue;  // define Qci type
GbrQosInformation qos;
qos.gbrDl = yourvalue; // Downlink GBR
qos.gbrUl = yourvalue; // Uplink GBR
qos.mbrDl = yourvalue; // Downlink MBR
qos.mbrUl = yourvalue; // Uplink MBR
EpsBearer bearer (q, qos);
lteHelper->ActivateEpsBearer (ueDevs, bearer, EpcTft::Default ());

In PSS, TBR is obtained from GBR in bearer level QoS parameters. In TBFQ, token generation rate is obtained from the MBR setting in bearer level QoS parameters, which therefore needs to be configured consistently. For constant bit rate (CBR) traffic, it is suggested to set MBR to GBR. For variance bit rate (VBR) traffic, it is suggested to set MBR k times larger than GBR in order to cover the peak traffic rate. In current implementation, k is set to three based on paper [FABokhari2009]. In addition, current version of TBFQ does not consider RLC header and PDCP header length in MBR and GBR. Another parameter in TBFQ is packet arrival rate. This parameter is calculated within scheduler and equals to the past average throughput which is used in PF scheduler.

Many useful attributes of the LTE-EPC model will be described in the following subsections. Still, there are many attributes which are not explicitly mentioned in the design or user documentation, but which are clearly documented using the ns-3 attribute system. You can easily print a list of the attributes of a given object together with their description and default value passing --PrintAttributes= to a simulation program, like this:

./waf --run lena-simple --command-template="%s --PrintAttributes=ns3::LteHelper"

You can try also with other LTE and EPC objects, like this:

./waf --run lena-simple --command-template="%s --PrintAttributes=ns3::LteEnbNetDevice"
./waf --run lena-simple --command-template="%s --PrintAttributes=ns3::LteEnbMac"
./waf --run lena-simple --command-template="%s --PrintAttributes=ns3::LteEnbPhy"
./waf --run lena-simple --command-template="%s --PrintAttributes=ns3::LteUePhy"
./waf --run lena-simple --command-template="%s --PrintAttributes=ns3::PointToPointEpcHelper"

Simulation Output

The ns-3 LTE model currently supports the output to file of PHY, MAC, RLC and PDCP level Key Performance Indicators (KPIs). You can enable it in the following way:

Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();

// configure all the simulation scenario here...

lteHelper->EnablePhyTraces ();
lteHelper->EnableMacTraces ();
lteHelper->EnableRlcTraces ();
lteHelper->EnablePdcpTraces ();

Simulator::Run ();

RLC and PDCP KPIs are calculated over a time interval and stored on ASCII files, two for RLC KPIs and two for PDCP KPIs, in each case one for uplink and one for downlink. The time interval duration can be controlled using the attribute ns3::RadioBearerStatsCalculator::EpochDuration.

The columns of the RLC KPI files is the following (the same for uplink and downlink):

  1. start time of measurement interval in seconds since the start of simulation
  2. end time of measurement interval in seconds since the start of simulation
  3. Cell ID
  4. unique UE ID (IMSI)
  5. cell-specific UE ID (RNTI)
  6. Logical Channel ID
  7. Number of transmitted RLC PDUs
  8. Total bytes transmitted.
  9. Number of received RLC PDUs
  10. Total bytes received
  11. Average RLC PDU delay in seconds
  12. Standard deviation of the RLC PDU delay
  13. Minimum value of the RLC PDU delay
  14. Maximum value of the RLC PDU delay
  15. Average RLC PDU size, in bytes
  16. Standard deviation of the RLC PDU size
  17. Minimum RLC PDU size
  18. Maximum RLC PDU size

Similarly, the columns of the PDCP KPI files is the following (again, the same for uplink and downlink):

  1. start time of measurement interval in seconds since the start of simulation
  2. end time of measurement interval in seconds since the start of simulation
  3. Cell ID
  4. unique UE ID (IMSI)
  5. cell-specific UE ID (RNTI)
  6. Logical Channel ID
  7. Number of transmitted PDCP PDUs
  8. Total bytes transmitted.
  9. Number of received PDCP PDUs
  10. Total bytes received
  11. Average PDCP PDU delay in seconds
  12. Standard deviation of the PDCP PDU delay
  13. Minimum value of the PDCP PDU delay
  14. Maximum value of the PDCP PDU delay
  15. Average PDCP PDU size, in bytes
  16. Standard deviation of the PDCP PDU size
  17. Minimum PDCP PDU size
  18. Maximum PDCP PDU size

MAC KPIs are basically a trace of the resource allocation reported by the scheduler upon the start of every subframe. They are stored in ASCII files. For downlink MAC KPIs the format is the following:

  1. Simulation time in seconds at which the allocation is indicated by the scheduler
  2. Cell ID
  3. unique UE ID (IMSI)
  4. Frame number
  5. Subframe number
  6. cell-specific UE ID (RNTI)
  7. MCS of TB 1
  8. size of TB 1
  9. MCS of TB 2 (0 if not present)
  10. size of TB 2 (0 if not present)

while for uplink MAC KPIs the format is:

  1. Simulation time in seconds at which the allocation is indicated by the scheduler
  2. Cell ID
  3. unique UE ID (IMSI)
  4. Frame number
  5. Subframe number
  6. cell-specific UE ID (RNTI)
  7. MCS of TB
  8. size of TB

The names of the files used for MAC KPI output can be customized via the ns-3 attributes ns3::MacStatsCalculator::DlOutputFilename and ns3::MacStatsCalculator::UlOutputFilename.

PHY KPIs are distributed in seven different files, configurable through the attributes

  1. ns3::PhyStatsCalculator::DlRsrpSinrFilename
  2. ns3::PhyStatsCalculator::UeSinrFilename
  3. ns3::PhyStatsCalculator::InterferenceFilename
  4. ns3::PhyStatsCalculator::DlTxOutputFilename
  5. ns3::PhyStatsCalculator::UlTxOutputFilename
  6. ns3::PhyStatsCalculator::DlRxOutputFilename
  7. ns3::PhyStatsCalculator::UlRxOutputFilename

In the RSRP/SINR file, the following content is available:

  1. Simulation time in seconds at which the allocation is indicated by the scheduler
  2. Cell ID
  3. unique UE ID (IMSI)
  4. RSRP
  5. Linear average over all RBs of the downlink SINR in linear units

The contents in the UE SINR file are:

  1. Simulation time in seconds at which the allocation is indicated by the scheduler
  2. Cell ID
  3. unique UE ID (IMSI)
  4. uplink SINR in linear units for the UE

In the interference filename the content is:

  1. Simulation time in seconds at which the allocation is indicated by the scheduler
  2. Cell ID
  3. List of interference values per RB

In UL and DL transmission files the parameters included are:

  1. Simulation time in milliseconds
  2. Cell ID
  3. unique UE ID (IMSI)
  4. RNTI
  5. Layer of transmission
  6. MCS
  7. size of the TB
  8. Redundancy version
  9. New Data Indicator flag

And finally, in UL and DL reception files the parameters included are:

  1. Simulation time in milliseconds
  2. Cell ID
  3. unique UE ID (IMSI)
  4. RNTI
  5. Transmission Mode
  6. Layer of transmission
  7. MCS
  8. size of the TB
  9. Redundancy version
  10. New Data Indicator flag
  11. Correctness in the reception of the TB

Fading Trace Usage

In this section we will describe how to use fading traces within LTE simulations.

Fading Traces Generation

It is possible to generate fading traces by using a dedicated matlab script provided with the code (/lte/model/fading-traces/fading-trace-generator.m). This script already includes the typical taps configurations for three 3GPP scenarios (i.e., pedestrian, vehicular and urban as defined in Annex B.2 of [TS36104]); however users can also introduce their specific configurations. The list of the configurable parameters is provided in the following:

  • fc : the frequency in use (it affects the computation of the doppler speed).
  • v_km_h : the speed of the users
  • traceDuration : the duration in seconds of the total length of the trace.
  • numRBs : the number of the resource block to be evaluated.
  • tag : the tag to be applied to the file generated.

The file generated contains ASCII-formatted real values organized in a matrix fashion: every row corresponds to a different RB, and every column correspond to a different temporal fading trace sample.

It has to be noted that the ns-3 LTE module is able to work with any fading trace file that complies with the above described ASCII format. Hence, other external tools can be used to generate custom fading traces, such as for example other simulators or experimental devices.

Fading Traces Usage

When using a fading trace, it is of paramount importance to specify correctly the trace parameters in the simulation, so that the fading model can load and use it correcly. The parameters to be configured are:

  • TraceFilename : the name of the trace to be loaded (absolute path, or relative path w.r.t. the path from where the simulation program is executed);
  • TraceLength : the trace duration in seconds;
  • SamplesNum : the number of samples;
  • WindowSize : the size of the fading sampling window in seconds;

It is important to highlight that the sampling interval of the fading trace has to be 1 ms or greater, and in the latter case it has to be an integer multiple of 1 ms in order to be correctly processed by the fading module.

The default configuration of the matlab script provides a trace 10 seconds long, made of 10,000 samples (i.e., 1 sample per TTI=1ms) and used with a windows size of 0.5 seconds amplitude. These are also the default values of the parameters above used in the simulator; therefore their settage can be avoided in case the fading trace respects them.

In order to activate the fading module (which is not active by default) the following code should be included in the simulation program:

Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
lteHelper->SetFadingModel("ns3::TraceFadingLossModel");

And for setting the parameters:

lteHelper->SetFadingModelAttribute ("TraceFilename", StringValue ("src/lte/model/fading-traces/fading_trace_EPA_3kmph.fad"));
lteHelper->SetFadingModelAttribute ("TraceLength", TimeValue (Seconds (10.0)));
lteHelper->SetFadingModelAttribute ("SamplesNum", UintegerValue (10000));
lteHelper->SetFadingModelAttribute ("WindowSize", TimeValue (Seconds (0.5)));
lteHelper->SetFadingModelAttribute ("RbNum", UintegerValue (100));

It has to be noted that, TraceFilename does not have a default value, therefore is has to be always set explicitly.

The simulator provide natively three fading traces generated according to the configurations defined in in Annex B.2 of [TS36104]. These traces are available in the folder src/lte/model/fading-traces/). An excerpt from these traces is represented in the following figures.

Fading trace 3 kmph

Excerpt of the fading trace included in the simulator for a pedestrian scenario (speed of 3 kmph).

Fading trace 60 kmph

Excerpt of the fading trace included in the simulator for a vehicular scenario (speed of 60 kmph).

Fading trace 3 kmph

Excerpt of the fading trace included in the simulator for an urban scenario (speed of 3 kmph).

Mobility Model with Buildings

We now explain by examples how to use the buildings model (in particular, the MobilityBuildingInfo and the BuildingPropagationModel classes) in an ns-3 simulation program to setup an LTE simulation scenario that includes buildings and indoor nodes.

  1. Header files to be included:

    #include <ns3/mobility-building-info.h>
    #include <ns3/buildings-propagation-loss-model.h>
    #include <ns3/building.h>
    
  2. Pathloss model selection:

    Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
    
    lteHelper->SetAttribute ("PathlossModel", StringValue ("ns3::BuildingsPropagationLossModel"));
    
  3. EUTRA Band Selection

The selection of the working frequency of the propagation model has to be done with the standard ns-3 attribute system as described in the correspond section (“Configuration of LTE model parameters”) by means of the DlEarfcn and UlEarfcn parameters, for instance:

lteHelper->SetEnbDeviceAttribute ("DlEarfcn", UintegerValue (100));
lteHelper->SetEnbDeviceAttribute ("UlEarfcn", UintegerValue (18100));

It is to be noted that using other means to configure the frequency used by the propagation model (i.e., configuring the corresponding BuildingsPropagationLossModel attributes directly) might generates conflicts in the frequencies definition in the modules during the simulation, and is therefore not advised.

  1. Mobility model selection:

    MobilityHelper mobility;
    mobility.SetMobilityModel ("ns3::ConstantPositionMobilityModel");
    
    It is to be noted that any mobility model can be used.
    
  2. Building creation:

    double x_min = 0.0;
    double x_max = 10.0;
    double y_min = 0.0;
    double y_max = 20.0;
    double z_min = 0.0;
    double z_max = 10.0;
    Ptr<Building> b = CreateObject <Building> ();
    b->SetBoundaries (Box (x_min, x_max, y_min, y_max, z_min, z_max));
    b->SetBuildingType (Building::Residential);
    b->SetExtWallsType (Building::ConcreteWithWindows);
    b->SetNFloors (3);
    b->SetNRoomsX (3);
    b->SetNRoomsY (2);
    

    This will instantiate a residential building with base of 10 x 20 meters and height of 10 meters whose external walls are of concrete with windows; the building has three floors and has an internal 3 x 2 grid of rooms of equal size.

  3. Node creation and positioning:

    ueNodes.Create (2);
    mobility.Install (ueNodes);
    BuildingsHelper::Install (ueNodes);
    NetDeviceContainer ueDevs;
    ueDevs = lteHelper->InstallUeDevice (ueNodes);
    Ptr<ConstantPositionMobilityModel> mm0 = enbNodes.Get (0)->GetObject<ConstantPositionMobilityModel> ();
    Ptr<ConstantPositionMobilityModel> mm1 = enbNodes.Get (1)->GetObject<ConstantPositionMobilityModel> ();
    mm0->SetPosition (Vector (5.0, 5.0, 1.5));
    mm1->SetPosition (Vector (30.0, 40.0, 1.5));
    
  4. Finalize the building and mobility model configuration:

    BuildingsHelper::MakeMobilityModelConsistent ();
    

See the documentation of the buildings module for more detailed information.

PHY Error Model

The Physical error model consists of the data error model and the downlink control error model, both of them active by default. It is possible to deactivate them with the ns3 attribute system, in detail:

Config::SetDefault ("ns3::LteSpectrumPhy::CtrlErrorModelEnabled", BooleanValue (false));
Config::SetDefault ("ns3::LteSpectrumPhy::DataErrorModelEnabled", BooleanValue (false));

MIMO Model

Is this subsection we illustrate how to configure the MIMO parameters. LTE defines 7 types of transmission modes:

  • Transmission Mode 1: SISO.
  • Transmission Mode 2: MIMO Tx Diversity.
  • Transmission Mode 3: MIMO Spatial Multiplexity Open Loop.
  • Transmission Mode 4: MIMO Spatial Multiplexity Closed Loop.
  • Transmission Mode 5: MIMO Multi-User.
  • Transmission Mode 6: Closer loop single layer precoding.
  • Transmission Mode 7: Single antenna port 5.

According to model implemented, the simulator includes the first three transmission modes types. The default one is the Transmission Mode 1 (SISO). In order to change the default Transmission Mode to be used, the attribute DefaultTransmissionMode of the LteEnbRrc can be used, as shown in the following:

Config::SetDefault ("ns3::LteEnbRrc::DefaultTransmissionMode", UintegerValue (0)); // SISO
Config::SetDefault ("ns3::LteEnbRrc::DefaultTransmissionMode", UintegerValue (1)); // MIMO Tx diversity (1 layer)
Config::SetDefault ("ns3::LteEnbRrc::DefaultTransmissionMode", UintegerValue (2)); // MIMO Spatial Multiplexity (2 layers)

For changing the transmission mode of a certain user during the simulation a specific interface has been implemented in both standard schedulers:

void TransmissionModeConfigurationUpdate (uint16_t rnti, uint8_t txMode);

This method can be used both for developing transmission mode decision engine (i.e., for optimizing the transmission mode according to channel condition and/or user’s requirements) and for manual switching from simulation script. In the latter case, the switching can be done as shown in the following:

Ptr<LteEnbNetDevice> lteEnbDev = enbDevs.Get (0)->GetObject<LteEnbNetDevice> ();
PointerValue ptrval;
enbNetDev->GetAttribute ("FfMacScheduler", ptrval);
Ptr<RrFfMacScheduler> rrsched = ptrval.Get<RrFfMacScheduler> ();
Simulator::Schedule (Seconds (0.2), &RrFfMacScheduler::TransmissionModeConfigurationUpdate, rrsched, rnti, 1);

Finally, the model implemented can be reconfigured according to different MIMO models by updating the gain values (the only constraints is that the gain has to be constant during simulation run-time and common for the layers). The gain of each Transmission Mode can be changed according to the standard ns3 attribute system, where the attributes are: TxMode1Gain, TxMode2Gain, TxMode3Gain, TxMode4Gain, TxMode5Gain, TxMode6Gain and TxMode7Gain. By default only TxMode1Gain, TxMode2Gain and TxMode3Gain have a meaningful value, that are the ones derived by _[CatreuxMIMO] (i.e., respectively 0.0, 4.2 and -2.8 dB).

Use of AntennaModel

We now show how associate a particular AntennaModel with an eNB device in order to model a sector of a macro eNB. For this purpose, it is convenient to use the CosineAntennaModel provided by the ns-3 antenna module. The configuration of the eNB is to be done via the LteHelper instance right before the creation of the EnbNetDevice, as shown in the following:

lteHelper->SetEnbAntennaModelType ("ns3::CosineAntennaModel");
lteHelper->SetEnbAntennaModelAttribute ("Orientation", DoubleValue (0));
lteHelper->SetEnbAntennaModelAttribute ("Beamwidth",   DoubleValue (60);
lteHelper->SetEnbAntennaModelAttribute ("MaxGain",     DoubleValue (0.0));

the above code will generate an antenna model with a 60 degrees beamwidth pointing along the X axis. The orientation is measured in degrees from the X axis, e.g., an orientation of 90 would point along the Y axis, and an orientation of -90 would point in the negative direction along the Y axis. The beamwidth is the -3 dB beamwidth, e.g, for a 60 degree beamwidth the antenna gain at an angle of \pm 30 degrees from the direction of orientation is -3 dB.

To create a multi-sector site, you need to create different ns-3 nodes placed at the same position, and to configure separate EnbNetDevice with different antenna orientations to be installed on each node.

Radio Environment Maps

By using the class RadioEnvironmentMapHelper it is possible to output to a file a Radio Environment Map (REM), i.e., a uniform 2D grid of values that represent the Signal-to-noise ratio in the downlink with respect to the eNB that has the strongest signal at each point.

To do this, you just need to add the following code to your simulation program towards the end, right before the call to Simulator::Run ():

Ptr<RadioEnvironmentMapHelper> remHelper = CreateObject<RadioEnvironmentMapHelper> ();
remHelper->SetAttribute ("ChannelPath", StringValue ("/ChannelList/0"));
remHelper->SetAttribute ("OutputFile", StringValue ("rem.out"));
remHelper->SetAttribute ("XMin", DoubleValue (-400.0));
remHelper->SetAttribute ("XMax", DoubleValue (400.0));
remHelper->SetAttribute ("XRes", UintegerValue (100));
remHelper->SetAttribute ("YMin", DoubleValue (-300.0));
remHelper->SetAttribute ("YMax", DoubleValue (300.0));
remHelper->SetAttribute ("YRes", UintegerValue (75));
remHelper->SetAttribute ("Z", DoubleValue (0.0));
remHelper->Install ();

By configuring the attributes of the RadioEnvironmentMapHelper object as shown above, you can tune the parameters of the REM to be generated. Note that each RadioEnvironmentMapHelper instance can generate only one REM; if you want to generate more REMs, you need to create one separate instance for each REM.

Note that the REM generation is very demanding, in particular:

  • the run-time memory consumption is approximately 5KB per pixel. For example, a REM with a resolution of 500x500 would need about 1.25 GB of memory, and a resolution of 1000x1000 would need needs about 5 GB (too much for a regular PC at the time of this writing). To overcome this issue, the REM is generated at successive steps, with each step evaluating at most a number of pixels determined by the value of the the attribute RadioEnvironmentMapHelper::MaxPointsPerIteration.
  • if you generate a REM at the beginning of a simulation, it will slow down the execution of the rest of the simulation. If you want to generate a REM for a program and also use the same program to get simulation result, it is recommended to add a command-line switch that allows to either generate the REM or run the complete simulation. For this purpose, note that there is an attribute RadioEnvironmentMapHelper::StopWhenDone (default: true) that will force the simulation to stop right after the REM has been generated.

The REM is stored in an ASCII file in the following format:

  • column 1 is the x coordinate
  • column 2 is the y coordinate
  • column 3 is the z coordinate
  • column 4 is the SINR in linear units

A minimal gnuplot script that allows you to plot the REM is given below:

set view map;
set xlabel "X"
set ylabel "Y"
set cblabel "SINR (dB)"
unset key
plot "rem.out" using ($1):($2):(10*log10($4)) with image

As an example, here is the REM that can be obtained with the example program lena-dual-stripe, which shows a three-sector LTE macrocell in a co-channel deployment with some residential femtocells randomly deployed in two blocks of apartments.

_images/lena-dual-stripe.png

REM obtained from the lena-dual-stripe example

Note that the lena-dual-stripe example program also generate gnuplot-compatible output files containing information about the positions of the UE and eNB nodes as well as of the buildings, respectively in the files ues.txt, enbs.txt and buildings.txt. These can be easily included when using gnuplot. For example, assuming that your gnuplot script (e.g., the minimal gnuplot script described above) is saved in a file named my_plot_script, running the following command would plot the location of UEs, eNBs and buildings on top of the REM:

gnuplot -p enbs.txt ues.txt buildings.txt my_plot_script

AMC Model and CQI Calculation

The simulator provides two possible schemes for what concerns the selection of the MCSs and correspondly the generation of the CQIs. The first one is based on the GSoC module [Piro2011] and works per RB basis. This model can be activated with the ns3 attribute system, as presented in the following:

Config::SetDefault ("ns3::LteAmc::AmcModel", EnumValue (LteAmc::PiroEW2010));

While, the solution based on the physical error model can be controlled with:

Config::SetDefault ("ns3::LteAmc::AmcModel", EnumValue (LteAmc::MiErrorModel));

Finally, the required efficiency of the PiroEW2010 AMC module can be tuned thanks to the Ber attribute (), for instance:

Config::SetDefault ("ns3::LteAmc::Ber", DoubleValue (0.00005));

Evolved Packet Core (EPC)

We now explain how to write a simulation program that allows to simulate the EPC in addition to the LTE radio access network. The use of EPC allows to use IPv4 networking with LTE devices. In other words, you will be able to use the regular ns-3 applications and sockets over IPv4 over LTE, and also to connect an LTE network to any other IPv4 network you might have in your simulation.

First of all, in addition to LteHelper that we already introduced in Basic simulation program, you need to use an additional EpcHelper class, which will take care of creating the EPC entities and network topology. Note that you can’t use EpcHelper directly, as it is an abstract base class; instead, you need to use one of its child classes, which provide different EPC topology implementations. In this example we will consider PointToPointEpcHelper, which implements an EPC based on point-to-point links. To use it, you need first to insert this code in your simulation program:

Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
Ptr<PointToPointEpcHelper> epcHelper = CreateObject<PointToPointEpcHelper> ();

Then, you need to tell the LTE helper that the EPC will be used:

lteHelper->SetEpcHelper (epcHelper);

the above step is necessary so that the LTE helper will trigger the appropriate EPC configuration in correspondance with some important configuration, such as when a new eNB or UE is added to the simulation, or an EPS bearer is created. The EPC helper will automatically take care of the necessary setup, such as S1 link creation and S1 bearer setup. All this will be done without the intervention of the user.

Calling lteHelper->SetEpcHelper (epcHelper) enables the use of EPC, and has the side effect that any new LteEnbRrc that is created will have the EpsBearerToRlcMapping attribute set to RLC_UM_ALWAYS instead of RLC_SM_ALWAYS if the latter was the default; otherwise, the attribute won’t be changed (e.g., if you changed the default to RLC_AM_ALWAYS, it won’t be touched).

It is to be noted that the EpcHelper will also automatically create the PGW node and configure it so that it can properly handle traffic from/to the LTE radio access network. Still, you need to add some explicit code to connect the PGW to other IPv4 networks (e.g., the internet). Here is a very simple example about how to connect a single remote host to the PGW via a point-to-point link:

Ptr<Node> pgw = epcHelper->GetPgwNode ();

 // Create a single RemoteHost
NodeContainer remoteHostContainer;
remoteHostContainer.Create (1);
Ptr<Node> remoteHost = remoteHostContainer.Get (0);
InternetStackHelper internet;
internet.Install (remoteHostContainer);

// Create the internet
PointToPointHelper p2ph;
p2ph.SetDeviceAttribute ("DataRate", DataRateValue (DataRate ("100Gb/s")));
p2ph.SetDeviceAttribute ("Mtu", UintegerValue (1500));
p2ph.SetChannelAttribute ("Delay", TimeValue (Seconds (0.010)));
NetDeviceContainer internetDevices = p2ph.Install (pgw, remoteHost);
Ipv4AddressHelper ipv4h;
ipv4h.SetBase ("1.0.0.0", "255.0.0.0");
Ipv4InterfaceContainer internetIpIfaces = ipv4h.Assign (internetDevices);
// interface 0 is localhost, 1 is the p2p device
Ipv4Address remoteHostAddr = internetIpIfaces.GetAddress (1);

It’s important to specify routes so that the remote host can reach LTE UEs. One way of doing this is by exploiting the fact that the PointToPointEpcHelper will by default assign to LTE UEs an IP address in the 7.0.0.0 network. With this in mind, it suffices to do:

Ipv4StaticRoutingHelper ipv4RoutingHelper;
Ptr<Ipv4StaticRouting> remoteHostStaticRouting = ipv4RoutingHelper.GetStaticRouting (remoteHost->GetObject<Ipv4> ());
remoteHostStaticRouting->AddNetworkRouteTo (Ipv4Address ("7.0.0.0"), Ipv4Mask ("255.0.0.0"), 1);

Now, you should go on and create LTE eNBs and UEs as explained in the previous sections. You can of course configure other LTE aspects such as pathloss and fading models. Right after you created the UEs, you should also configure them for IP networking. This is done as follows. We assume you have a container for UE and eNodeB nodes like this:

NodeContainer ueNodes;
NodeContainer enbNodes;

to configure an LTE-only simulation, you would then normally do something like this:

NetDeviceContainer ueLteDevs = lteHelper->InstallUeDevice (ueNodes);
lteHelper->Attach (ueLteDevs, enbLteDevs.Get (0));

in order to configure the UEs for IP networking, you just need to additionally do like this:

// we install the IP stack on the UEs
InternetStackHelper internet;
internet.Install (ueNodes);

// assign IP address to UEs
for (uint32_t u = 0; u < ueNodes.GetN (); ++u)
  {
    Ptr<Node> ue = ueNodes.Get (u);
    Ptr<NetDevice> ueLteDevice = ueLteDevs.Get (u);
    Ipv4InterfaceContainer ueIpIface = epcHelper->AssignUeIpv4Address (NetDeviceContainer (ueLteDevice));
    // set the default gateway for the UE
    Ptr<Ipv4StaticRouting> ueStaticRouting = ipv4RoutingHelper.GetStaticRouting (ue->GetObject<Ipv4> ());
    ueStaticRouting->SetDefaultRoute (epcHelper->GetUeDefaultGatewayAddress (), 1);
  }

The activation of bearers is done in a slightly different way with respect to what done for an LTE-only simulation. First, the method ActivateDataRadioBearer is not to be used when the EPC is used. Second, when EPC is used, the default EPS bearer will be activated automatically when you call LteHelper::Attach (). Third, if you want to setup dedicated EPS bearer, you can do so using the method LteHelper::ActivateDedicatedEpsBearer (). This method takes as a parameter the Traffic Flow Template (TFT), which is a struct that identifies the type of traffic that will be mapped to the dedicated EPS bearer. Here is an example for how to setup a dedicated bearer for an application at the UE communicating on port 1234:

Ptr<EpcTft> tft = Create<EpcTft> ();
EpcTft::PacketFilter pf;
pf.localPortStart = 1234;
pf.localPortEnd = 1234;
tft->Add (pf);
lteHelper->ActivateEpsBearer (ueLteDevs, EpsBearer (EpsBearer::NGBR_VIDEO_TCP_DEFAULT), pf);

you can of course use custom EpsBearer and EpcTft configurations, please refer to the doxygen documentation for how to do it.

Finally, you can install applications on the LTE UE nodes that communicate with remote applications over the internet. This is done following the usual ns-3 procedures. Following our simple example with a single remoteHost, here is how to setup downlink communication, with an UdpClient application on the remote host, and a PacketSink on the LTE UE (using the same variable names of the previous code snippets)

uint16_t dlPort = 1234;
PacketSinkHelper packetSinkHelper ("ns3::UdpSocketFactory",
                                   InetSocketAddress (Ipv4Address::GetAny (), dlPort));
ApplicationContainer serverApps = packetSinkHelper.Install (ue);
serverApps.Start (Seconds (0.01));
UdpClientHelper client (ueIpIface.GetAddress (0), dlPort);
ApplicationContainer clientApps = client.Install (remoteHost);
clientApps.Start (Seconds (0.01));

That’s all! You can now start your simulation as usual:

Simulator::Stop (Seconds (10.0));
Simulator::Run ();

Network Attachment

As shown in the basic example in section Basic simulation program, attaching a UE to an eNodeB is done by calling LteHelper::Attach function.

There are 2 possible ways of network attachment. The first method is the “manual” one, while the second one has a more “automatic” sense on it. Each of them will be covered in this section.

Manual attachment

This method uses the LteHelper::Attach function mentioned above. It has been the only available network attachment method in earlier versions of LTE module. It is typically invoked before the simulation begins:

lteHelper->Attach (ueDevs, enbDev); // attach one or more UEs to a single eNodeB

LteHelper::InstallEnbDevice and LteHelper::InstallUeDevice functions must have been called before attaching. In an EPC-enabled simulation, it is also required to have IPv4 properly pre-installed in the UE.

This method is very simple, but requires you to know exactly which UE belongs to to which eNodeB before the simulation begins. This can be difficult when the UE initial position is randomly determined by the simulation script.

One may choose the distance between the UE and the eNodeB as a criterion for selecting the appropriate cell. It is quite simple (at least from the simulator’s point of view) and sometimes practical. But it is important to note that sometimes distance does not make a single correct criterion. For instance, the eNodeB antenna directivity should be considered as well. Besides that, one should also take into account the channel condition, which might be fluctuating if there is fading or shadowing in effect. In these kind of cases, network attachment should not be based on distance alone.

In real life, UE will automatically evaluate certain criteria and select the best cell to attach to, without manual intervention from the user. Obviously this is not the case in this LteHelper::Attach function. The other network attachment method uses more “automatic” approach to network attachment, as will be described next.

Automatic attachment using Idle mode cell selection procedure

The strength of the received signal is the standard criterion used for selecting the best cell to attach to. The use of this criterion is implemented in the initial cell selection process, which can be invoked by calling another version of the LteHelper::Attach function, as shown below:

lteHelper->Attach (ueDevs); // attach one or more UEs to a strongest cell

The difference with the manual method is that the destination eNodeB is not specified. The procedure will find the best cell for the UEs, based on several criteria, including the strength of the received signal (RSRP).

After the method is called, the UE will spend some time to measure the neighbouring cells, and then attempt to attach to the best one. More details can be found in section Initial Cell Selection of the Design Documentation.

It is important to note that this method only works in EPC-enabled simulations. LTE-only simulations must resort to manual attachment method.

Closed Subscriber Group

An interesting use case of the initial cell selection process is to setup a simulation environment with Closed Subscriber Group (CSG).

For example, a certain eNodeB, typically a smaller version such as femtocell, might belong to a private owner (e.g. a household or business), allowing access only to some UEs which have been previously registered by the owner. The eNodeB and the registered UEs altogether form a CSG.

The access restriction can be simulated by “labeling” the CSG members with the same CSG ID. This is done through the attributes in both eNodeB and UE, for example using the following LteHelper functions:

// label the following eNodeBs with CSG identity of 1 and CSG indication enabled
lteHelper->SetEnbDeviceAttribute ("CsgId", UintegerValue (1));
lteHelper->SetEnbDeviceAttribute ("CsgIndication", BooleanValue (true));

// label one or more UEs with CSG identity of 1
lteHelper->SetUeDeviceAttribute ("CsgId", UintegerValue (1));

// install the eNodeBs and UEs
NetDeviceContainer csgEnbDevs = lteHelper->InstallEnbDevice (csgEnbNodes);
NetDeviceContainer csgUeDevs = lteHelper->InstallUeDevice (csgUeNodes);

Then enable the initial cell selection procedure on the UEs:

lteHelper->Attach (csgUeDevs);

This is necessary because the CSG restriction only works with automatic method of network attachment, but not in the manual method.

Note that setting the CSG indication of an eNodeB as false (the default value) will disable the restriction, i.e., any UEs can connect to this eNodeB.

Configure UE measurements

The active UE measurement configuration in a simulation is dictated by the selected so called “consumers”, such as handover algorithm. Users may add their own configuration into action, and there are several ways to do so:

  1. direct configuration in eNodeB RRC entity;
  2. configuring existing handover algorithm; and
  3. developing a new handover algorithm.

This section will cover the first method only. The second method is covered in Automatic handover trigger, while the third method is explained in length in Section Handover algorithm of the Design Documentation.

Direct configuration in eNodeB RRC works as follows. User begins by creating a new LteRrcSap::ReportConfigEutra instance and pass it to the LteEnbRrc::AddUeMeasReportConfig function. The function will return the measId (measurement identity) which is a unique reference of the configuration in the eNodeB instance. This function must be called before the simulation begins. The measurement configuration will be active in all UEs attached to the eNodeB throughout the duration of the simulation. During the simulation, user can capture the measurement reports produced by the UEs by listening to the existing LteEnbRrc::RecvMeasurementReport trace source.

The structure ReportConfigEutra is in accord with 3GPP specification. Definition of the structure and each member field can be found in Section 6.3.5 of [TS36331].

The code sample below configures Event A1 RSRP measurement to every eNodeB within the container devs:

LteRrcSap::ReportConfigEutra config;
config.eventId = LteRrcSap::ReportConfigEutra::EVENT_A1;
config.threshold1.choice = LteRrcSap::ThresholdEutra::THRESHOLD_RSRP;
config.threshold1.range = 41;
config.triggerQuantity = LteRrcSap::ReportConfigEutra::RSRP;
config.reportInterval = LteRrcSap::ReportConfigEutra::MS480;

std::vector<uint8_t> measIdList;

NetDeviceContainer::Iterator it;
for (it = devs.Begin (); it != devs.End (); it++)
{
  Ptr<NetDevice> dev = *it;
  Ptr<LteEnbNetDevice> enbDev = dev->GetObject<LteEnbNetDevice> ();
  Ptr<LteEnbRrc> enbRrc = enbDev->GetRrc ();

  uint8_t measId = enbRrc->AddUeMeasReportConfig (config);
  measIdList.push_back (measId); // remember the measId created

  enbRrc->TraceConnect ("RecvMeasurementReport",
                        "context",
                        MakeCallback (&RecvMeasurementReportCallback));
}

Note that thresholds are expressed as range. In the example above, the range 41 for RSRP corresponds to -100 dBm. The conversion from and to the range format is due to Section 9.1.4 and 9.1.7 of [TS36133]. The EutranMeasurementMapping class has several static functions that can be used for this purpose.

The corresponding callback function would have a definition similar as below:

void
RecvMeasurementReportCallback (std::string context,
                               uint64_t imsi,
                               uint16_t cellId,
                               uint16_t rnti,
                               LteRrcSap::MeasurementReport measReport);

This method will register the callback function as a consumer of UE measurements. In the case where there are more than one consumers in the simulation (e.g. handover algorithm), the measurements intended for other consumers will also be captured by this callback function. Users may utilize the the measId field, contained within the LteRrcSap::MeasurementReport argument of the callback function, to tell which measurement configuration has triggered the report.

In general, this mechanism prevents one consumer to unknowingly intervene with another consumer’s reporting configuration.

Note that only the reporting configuration part (i.e. LteRrcSap::ReportConfigEutra) of the UE measurements parameter is open for consumers to configure, while the other parts are kept hidden. The intra-frequency limitation is the main motivation behind this API implementation decision:

  • there is only one, unambiguous and definitive measurement object, thus there is no need to configure it;
  • measurement identities are kept hidden because of the fact that there is one-to-one mapping between reporting configuration and measurement identity, thus a new measurement identity is set up automatically when a new reporting configuration is created;
  • quantity configuration is configured elsewhere, see Performing measurements; and
  • measurement gaps are not supported, because it is only applicable for inter-frequency settings;

X2-based handover

As defined by 3GPP, handover is a procedure for changing the serving cell of a UE in CONNECTED mode. The two eNodeBs involved in the process are typically called the source eNodeB and the target eNodeB.

In order to enable the execution of X2-based handover in simulation, there are two requirements that must be met. Firstly, EPC must be enabled in the simulation (see Evolved Packet Core (EPC)).

Secondly, an X2 interface must be configured between the two eNodeBs, which needs to be done explicitly within the simulation program:

lteHelper->AddX2Interface (enbNodes);

where enbNodes is a NodeContainer that contains the two eNodeBs between which the X2 interface is to be configured. If the container has more than two eNodeBs, the function will create an X2 interface between every pair of eNodeBs in the container.

Lastly, the target eNodeB must be configured as “open” to X2 HANDOVER REQUEST. Every eNodeB is open by default, so no extra instruction is needed in most cases. However, users may set the eNodeB to “closed” by setting the boolean attribute LteEnbRrc::AdmitHandoverRequest to false. As an example, you can run the lena-x2-handover program and setting the attribute in this way:

NS_LOG=EpcX2:LteEnbRrc ./waf --run lena-x2-handover --command="%s --ns3::LteEnbRrc::AdmitHandoverRequest=false"

After the above three requirements are fulfilled, the handover procedure can be triggered manually or automatically. Each will be presented in the following subsections.

Manual handover trigger

Handover event can be triggered “manually” within the simulation program by scheduling an explicit handover event. The LteHelper object provides a convenient method for the scheduling of a handover event. As an example, let us assume that ueLteDevs is a NetDeviceContainer that contains the UE that is to be handed over, and that enbLteDevs is another NetDeviceContainer that contains the source and the target eNB. Then, a handover at 0.1s can be scheduled like this:

lteHelper->HandoverRequest (Seconds (0.100),
                            ueLteDevs.Get (0),
                            enbLteDevs.Get (0),
                            enbLteDevs.Get (1));

Note that the UE needs to be already connected to the source eNB, otherwise the simulation will terminate with an error message.

For an example with full source code, please refer to the lena-x2-handover example program.

Automatic handover trigger

Handover procedure can also be triggered “automatically” by the serving eNodeB of the UE. The logic behind the trigger depends on the handover algorithm currently active in the eNodeB RRC entity. Users may select and configure the handover algorithm that will be used in the simulation, which will be explained shortly in this section. Users may also opt to write their own implementation of handover algorithm, as described in Section Handover algorithm of the Design Documentation.

Selecting a handover algorithm is done via the LteHelper object and its SetHandoverAlgorithmType method as shown below:

Ptr<LteHelper> lteHelper = CreateObject<LteHelper> ();
lteHelper->SetHandoverAlgorithmType ("ns3::A2A4RsrqHandoverAlgorithm");

The selected handover algorithm may also provide several configurable attributes, which can be set as follows:

lteHelper->SetHandoverAlgorithmAttribute ("ServingCellThreshold",
                                          UintegerValue (30));
lteHelper->SetHandoverAlgorithmAttribute ("NeighbourCellOffset",
                                          UintegerValue (1));

Three options of handover algorithm are included in the LTE module. The A2-A4-RSRQ handover algorithm (named as ns3::A2A4RsrqHandoverAlgorithm) is the default option, and the usage has already been shown above.

Another option is the strongest cell handover algorithm (named as ns3::A3RsrpHandoverAlgorithm), which can be selected and configured by the following code:

lteHelper->SetHandoverAlgorithmType ("ns3::A3RsrpHandoverAlgorithm");
lteHelper->SetHandoverAlgorithmAttribute ("Hysteresis",
                                          DoubleValue (3.0));
lteHelper->SetHandoverAlgorithmAttribute ("TimeToTrigger",
                                          TimeValue (MilliSeconds (256)));

The last option is a special one, called the no-op handover algorithm, which basically disables automatic handover trigger. This is useful for example in cases where manual handover trigger need an exclusive control of all handover decision. It does not have any configurable attributes. The usage is as follows:

lteHelper->SetHandoverAlgorithmType ("ns3::NoOpHandoverAlgorithm");

For more information on each handover algorithm’s decision policy and their attributes, please refer to their respective subsections in Section Handover algorithm of the Design Documentation.

Finally, the InstallEnbDevice function of LteHelper will instantiate one instance of the selected handover algorithm for each eNodeB device. In other words, make sure to select the right handover algorithm before finalizing it in the following line of code:

NetDeviceContainer enbLteDevs = lteHelper->InstallEnbDevice (enbNodes);

Example with full source code of using automatic handover trigger can be found in the lena-x2-handover-measures example program.

Tuning simulation with handover

As mentioned in the Design Documentation, the current implementation of handover model may produce unpredicted behaviour when handover failure occurs. This subsection will focus on the steps that should be taken into account by users if they plan to use handover in their simulations.

The major cause of handover failure that we will tackle is the error in transmitting handover-related signaling messages during the execution of a handover procedure. As apparent from the Figure Sequence diagram of the X2-based handover from the Design Documentation, there are many of them and they use different interfaces and protocols. For the sake of simplicity, we can safely assume that the X2 interface (between the source eNodeB and the target eNodeB) and the S1 interface (between the target eNodeB and the SGW/PGW) are quite stable. Therefore we will focus our attention to the RRC protocol (between the UE and the eNodeBs) and the Random Access procedure, which are normally transmitted through the air and susceptible to degradation of channel condition.

A general tips to reduce transmission error is to ensure high enough SINR level in every UE. This can be done by a proper planning of the network topology that minimizes network coverage hole. If the topology has a known coverage hole, then the UE should be configured not to venture to that area.

Another approach to keep in mind is to avoid too-late handovers. In other words, handover should happen before the UE’s SINR becomes too low, otherwise the UE may fail to receive the handover command from the source eNodeB. Handover algorithms have the means to control how early or late a handover decision is made. For example, A2-A4-RSRQ handover algorithm can be configured with a higher threshold to make it decide a handover earlier. Similarly, smaller hysteresis and/or shorter time-to-trigger in the strongest cell handover algorithm typically results in earlier handovers. In order to find the right values for these parameters, one of the factors that should be considered is the UE movement speed. Generally, a faster moving UE requires the handover to be executed earlier. Some research work have suggested recommended values, such as in [Lee2010].

The above tips should be enough in normal simulation uses, but in the case some special needs arise then an extreme measure can be taken into consideration. For instance, users may consider disabling the channel error models. This will ensure that all handover-related signaling messages will be transmitted successfully, regardless of distance and channel condition. However, it will also affect all other data or control packets not related to handover, which may be an unwanted side effect. Otherwise, it can be done as follows:

Config::SetDefault ("ns3::LteSpectrumPhy::CtrlErrorModelEnabled");
Config::SetDefault ("ns3::LteSpectrumPhy::DataErrorModelEnabled");

By using the above code, we disable the error model in both control and data channels and in both directions (downlink and uplink). This is necessary because handover-related signaling messages are transmitted using these channels. An exception is when the simulation uses the ideal RRC protocol. In this case, only the Random Access procedure is left to be considered. The procedure consists of control messages, therefore we only need to disable the control channel’s error model.

Handover traces

The RRC model, in particular the LteEnbRrc and LteUeRrc objects, provide some useful traces which can be hooked up to some custom functions so that they are called upon start and end of the handover execution phase at both the UE and eNB side. As an example, in your simulation program you can declare the following methods:

void
NotifyHandoverStartUe (std::string context,
                       uint64_t imsi,
                       uint16_t cellId,
                       uint16_t rnti,
                       uint16_t targetCellId)
{
  std::cout << Simulator::Now ().GetSeconds () << " " << context
            << " UE IMSI " << imsi
            << ": previously connected to CellId " << cellId
            << " with RNTI " << rnti
            << ", doing handover to CellId " << targetCellId
            << std::endl;
}

void
NotifyHandoverEndOkUe (std::string context,
                       uint64_t imsi,
                       uint16_t cellId,
                       uint16_t rnti)
{
  std::cout << Simulator::Now ().GetSeconds () << " " << context
            << " UE IMSI " << imsi
            << ": successful handover to CellId " << cellId
            << " with RNTI " << rnti
            << std::endl;
}

void
NotifyHandoverStartEnb (std::string context,
                        uint64_t imsi,
                        uint16_t cellId,
                        uint16_t rnti,
                        uint16_t targetCellId)
{
  std::cout << Simulator::Now ().GetSeconds () << " " << context
            << " eNB CellId " << cellId
            << ": start handover of UE with IMSI " << imsi
            << " RNTI " << rnti
            << " to CellId " << targetCellId
            << std::endl;
}

void
NotifyHandoverEndOkEnb (std::string context,
                        uint64_t imsi,
                        uint16_t cellId,
                        uint16_t rnti)
{
  std::cout << Simulator::Now ().GetSeconds () << " " << context
            << " eNB CellId " << cellId
            << ": completed handover of UE with IMSI " << imsi
            << " RNTI " << rnti
            << std::endl;
}

Then, you can hook up these methods to the corresponding trace sources like this:

Config::Connect ("/NodeList/*/DeviceList/*/LteEnbRrc/HandoverStart",
                 MakeCallback (&NotifyHandoverStartEnb));
Config::Connect ("/NodeList/*/DeviceList/*/LteUeRrc/HandoverStart",
                 MakeCallback (&NotifyHandoverStartUe));
Config::Connect ("/NodeList/*/DeviceList/*/LteEnbRrc/HandoverEndOk",
                 MakeCallback (&NotifyHandoverEndOkEnb));
Config::Connect ("/NodeList/*/DeviceList/*/LteUeRrc/HandoverEndOk",
                 MakeCallback (&NotifyHandoverEndOkUe));

The example program src/lte/examples/lena-x2-handover.cc illustrates how the all above instructions can be integrated in a simulation program. You can run the program like this:

./waf --run lena-x2-handover

and it will output the messages printed by the custom handover trace hooks. In order additionally visualize some meaningful logging information, you can run the program like this:

NS_LOG=LteEnbRrc:LteUeRrc:EpcX2 ./waf --run lena-x2-handover

Examples Programs

The directory src/lte/examples/ contains some example simulation programs that show how to simulate different LTE scenarios.

Reference scenarios

There is a vast amount of reference LTE simulation scenarios which can be found in the literature. Here we list some of them:

  • The system simulation scenarios mentioned in section A.2 of [TR36814].

  • The dual stripe model [R4-092042], which is partially implemented in the example program src/lte/examples/lena-dual-stripe.cc. This example program features a lot of configurable parameters which can be customized by changing the corresponding global variables. To get a list of all these global variables, you can run this command:

    ./waf --run lena-dual-stripe --command-template="%s --PrintGlobals"
    

    The following subsection presents an example of running a simulation campaign using this example program.

Handover simulation campaign

In this subsection, we will demonstrate an example of running a simulation campaign using the LTE module of ns-3. The objective of the campaign is to compare the effect of each built-in handover algorithm of the LTE module.

The campaign will use the lena-dual-stripe example program. First, we have to modify the example program to produce the output that we need. In this occassion, we want to produce the number of handovers, user average throughput, and average SINR.

The number of handovers can be obtained by counting the number of times the HandoverEndOk Handover traces is fired. Then the user average throughput can be obtained by enabling the RLC Simulation Output. Finally, SINR can be obtained by enabling the PHY simulation output. The following sample code snippet shows one possible way to obtain the above:

void
NotifyHandoverEndOkUe (std::string context, uint64_t imsi,
                       uint16_t cellId, uint16_t rnti)
{
  std::cout << "Handover IMSI " << imsi << std::endl;
}

int
main (int argc, char *argv[])
{
  /*** SNIP ***/

  Config::Connect ("/NodeList/*/DeviceList/*/LteUeRrc/HandoverEndOk",
                   MakeCallback (&NotifyHandoverEndOkUe));

  lteHelper->EnablePhyTraces ();
  lteHelper->EnableRlcTraces ();
  Ptr<RadioBearerStatsCalculator> rlcStats = lteHelper->GetRlcStats ();
  rlcStats->SetAttribute ("StartTime", TimeValue (Seconds (0)));
  rlcStats->SetAttribute ("EpochDuration", TimeValue (Seconds (simTime)));

  Simulator::Run ();
  Simulator::Destroy ();
  return 0;
}

Then we have to configure the parameters of the program to suit our simulation needs. We are looking for the following assumptions in our simulation:

  • 7 sites of tri-sectored macro eNodeBs (i.e. 21 macrocells) deployed in hexagonal layout with 500 m inter-site distance.
  • Although lena-dual-stripe is originally intended for a two-tier (macrocell and femtocell) simulation, we will simplify our simulation to one-tier (macrocell) simulation only.
  • UEs are randomly distributed around the sites and attach to the network automatically using Idle mode cell selection. After that, UE will roam the simulation environment with 60 kmph movement speed.
  • 50 seconds simulation duration, so UEs would have traveled far enough to trigger some handovers.
  • 46 dBm macrocell Tx power and 10 dBm UE Tx power.
  • EPC mode will be used because the X2 handover procedure requires it to be enabled.
  • Full-buffer downlink and uplink traffic, both in 5 MHz bandwidth, using TCP protocol and Proportional Fair scheduler.
  • Ideal RRC protocol.

Table lena-dual-stripe parameter configuration for handover campaign below shows how we configure the parameters of lena-dual-stripe to achieve the above assumptions.

lena-dual-stripe parameter configuration for handover campaign
Parameter name Value Description
simTime 50 50 seconds simulation duration
nBlocks 0 Disabling apartment buildings and femtocells
nMacroEnbSites 7 Number of macrocell sites (each site has 3 cells)
nMacroEnbSitesX 2 The macrocell sites will be positioned in a 2-3-2 formation
interSiteDistance 500 500 m distance between adjacent macrocell sites
macroEnbTxPowerDbm 46 46 dBm Tx power for each macrocell
epc 1 Enable EPC mode
epcDl 1 Enable full-buffer DL traffic
epcUl 1 Enable full-buffer UL traffic
useUdp 0 Disable UDP traffic and enable TCP instead
macroUeDensity 0.00002 Determines number of UEs (translates to 48 UEs in our simulation)
outdoorUeMinSpeed 16.6667 Minimum UE movement speed in m/s (60 kmph)
outdoorUeMaxSpeed 16.6667 Maximum UE movement speed in m/s (60 kmph)
macroEnbBandwidth 25 5 MHz DL and UL bandwidth
generateRem 1 (Optional) For plotting the Radio Environment Map

Some of the required assumptions are not available as parameters of lena-dual-stripe. In this case, we override the default attributes, as shown in Table Overriding default attributes for handover campaign below.

Overriding default attributes for handover campaign
Default value name Value Description
ns3::LteHelper::HandoverAlgorithm ns3::NoOpHandoverAlgorithm, ns3::A3RsrpHandoverAlgorithm, or ns3::A2A4RsrqHandoverAlgorithm Choice of handover algorithm
ns3::LteHelper::Scheduler ns3::PfFfMacScheduler Proportional Fair scheduler
ns3::LteHelper::UseIdealRrc 1 Ideal RRC protocol
ns3::RadioBearerStatsCalculator::DlRlcOutputFilename <run>-DlRlcStats.txt File name for DL RLC trace output
ns3::RadioBearerStatsCalculator::UlRlcOutputFilename <run>-UlRlcStats.txt File name for UL RLC trace output
ns3::PhyStatsCalculator::DlRsrpSinrFilename <run>-DlRsrpSinrStats.txt File name for DL PHY RSRP/SINR trace output
ns3::PhyStatsCalculator::UlSinrFilename <run>-UlSinrStats.txt File name for UL PHY SINR trace output

ns-3 provides many ways for passing configuration values into a simulation. In this example, we will use the command line arguments. It is basically done by appending the parameters and their values to the waf call when starting each individual simulation. So the waf calls for invoking our 3 simulations would look as below:

$ ./waf --run="lena-dual-stripe
  --simTime=50 --nBlocks=0 --nMacroEnbSites=7 --nMacroEnbSitesX=2
  --epc=1 --useUdp=0 --outdoorUeMinSpeed=16.6667 --outdoorUeMaxSpeed=16.6667
  --ns3::LteHelper::HandoverAlgorithm=ns3::NoOpHandoverAlgorithm
  --ns3::RadioBearerStatsCalculator::DlRlcOutputFilename=no-op-DlRlcStats.txt
  --ns3::RadioBearerStatsCalculator::UlRlcOutputFilename=no-op-UlRlcStats.txt
  --ns3::PhyStatsCalculator::DlRsrpSinrFilename=no-op-DlRsrpSinrStats.txt
  --ns3::PhyStatsCalculator::UlSinrFilename=no-op-UlSinrStats.txt
  --RngRun=1" > no-op.txt

$ ./waf --run="lena-dual-stripe
  --simTime=50 --nBlocks=0 --nMacroEnbSites=7 --nMacroEnbSitesX=2
  --epc=1 --useUdp=0 --outdoorUeMinSpeed=16.6667 --outdoorUeMaxSpeed=16.6667
  --ns3::LteHelper::HandoverAlgorithm=ns3::A3RsrpHandoverAlgorithm
  --ns3::RadioBearerStatsCalculator::DlRlcOutputFilename=a3-rsrp-DlRlcStats.txt
  --ns3::RadioBearerStatsCalculator::UlRlcOutputFilename=a3-rsrp-UlRlcStats.txt
  --ns3::PhyStatsCalculator::DlRsrpSinrFilename=a3-rsrp-DlRsrpSinrStats.txt
  --ns3::PhyStatsCalculator::UlSinrFilename=a3-rsrp-UlSinrStats.txt
  --RngRun=1" > a3-rsrp.txt

$ ./waf --run="lena-dual-stripe
  --simTime=50 --nBlocks=0 --nMacroEnbSites=7 --nMacroEnbSitesX=2
  --epc=1 --useUdp=0 --outdoorUeMinSpeed=16.6667 --outdoorUeMaxSpeed=16.6667
  --ns3::LteHelper::HandoverAlgorithm=ns3::A2A4RsrqHandoverAlgorithm
  --ns3::RadioBearerStatsCalculator::DlRlcOutputFilename=a2-a4-rsrq-DlRlcStats.txt
  --ns3::RadioBearerStatsCalculator::UlRlcOutputFilename=a2-a4-rsrq-UlRlcStats.txt
  --ns3::PhyStatsCalculator::DlRsrpSinrFilename=a2-a4-rsrq-DlRsrpSinrStats.txt
  --ns3::PhyStatsCalculator::UlSinrFilename=a2-a4-rsrq-UlSinrStats.txt
  --RngRun=1" > a2-a4-rsrq.txt

Some notes on the execution:

  • Notice that some arguments are not specified because they are already the same as the default values. We also keep the handover algorithms on each own default settings.
  • Note the file names of simulation output, e.g. RLC traces and PHY traces, because we have to make sure that they are not overwritten by the next simulation run. In this example, we specify the names one by one using the command line arguments.
  • The --RngRun=1 argument at the end is used for setting the run number used by the random number generator used in the simulation. We re-run the same simulations with different RngRun values, hence creating several independent replications of the same simulations. Then we average the results obtained from these replications to achieve some statistical confidence.
  • We can add a --generateRem=1 argument to generate the files necessary for generating the Radio Environment Map (REM) of the simulation. The result is Figure REM obtained from a simulation in handover campaign below, which can be produced by following the steps described in Section Radio Environment Maps. This figure also shows the position of eNodeBs and UEs at the beginning of a simulation using RngRun = 1. Other values of RngRun may produce different UE position.
_images/lte-handover-campaign-rem.png

REM obtained from a simulation in handover campaign

After hours of running, the simulation campaign will eventually end. Next we will perform some post-processing on the produced simulation output to obtain meaningful information out of it.

In this example, we use GNU Octave to assist the processing of throughput and SINR data, as demonstrated in a sample GNU Octave script below:

% RxBytes is the 10th column
DlRxBytes = load ("no-op-DlRlcStats.txt") (:,10);
DlAverageThroughputKbps = sum (DlRxBytes) * 8 / 1000 / 50

% RxBytes is the 10th column
UlRxBytes = load ("no-op-UlRlcStats.txt") (:,10);
UlAverageThroughputKbps = sum (UlRxBytes) * 8 / 1000 / 50

% Sinr is the 6th column
DlSinr = load ("no-op-DlRsrpSinrStats.txt") (:,6);
% eliminate NaN values
idx = isnan (DlSinr);
DlSinr (idx) = 0;
DlAverageSinrDb = 10 * log10 (mean (DlSinr)) % convert to dB

% Sinr is the 5th column
UlSinr = load ("no-op-UlSinrStats.txt") (:,5);
% eliminate NaN values
idx = isnan (UlSinr);
UlSinr (idx) = 0;
UlAverageSinrDb = 10 * log10 (mean (UlSinr)) % convert to dB

As for the number of handovers, we can use simple shell scripting to count the number of occurrences of string “Handover” in the log file:

$ grep "Handover" no-op.txt | wc -l

Table Results of handover campaign below shows the complete statistics after we are done with post-processing on every individual simulation run. The values shown are the average of the results obtained from RngRun of 1, 2, 3, and 4.

Results of handover campaign
Statistics No-op A2-A4-RSRQ Strongest cell
Average DL system throughput 6 615 kbps 20 509 kbps 19 709 kbps
Average UL system throughput 4 095 kbps 5 705 kbps 6 627 kbps
Average DL SINR -0.10 dB 5.19 dB 5.24 dB
Average UL SINR 9.54 dB 81.57 dB 79.65 dB
Number of handovers per UE per second 0 0.05694 0.04771

The results show that having a handover algorithm in a mobility simulation improves both user throughput and SINR significantly. There is little difference between the two handover algorithms in this campaign scenario. It would be interesting to see their performance in different scenarios, such as scenarios with home eNodeBs deployment.

Testing Documentation

Overview

To test and validate the ns-3 LTE module, several test suites are provided which are integrated with the ns-3 test framework. To run them, you need to have configured the build of the simulator in this way:

$ ./waf configure --enable-tests --enable-modules=lte --enable-examples
$ ./test.py

The above will run not only the test suites belonging to the LTE module, but also those belonging to all the other ns-3 modules on which the LTE module depends. See the ns-3 manual for generic information on the testing framework.

You can get a more detailed report in HTML format in this way:

$ ./test.py -w results.html

After the above command has run, you can view the detailed result for each test by opening the file results.html with a web browser.

You can run each test suite separately using this command:

$ ./test.py -s test-suite-name

For more details about test.py and the ns-3 testing framework, please refer to the ns-3 manual.

Description of the test suites

Unit Tests
E-UTRA Absolute Radio Frequency Channel Number (EARFCN)

The test suite lte-earfcn checks that the carrier frequency used by the LteSpectrumValueHelper class (which implements the LTE spectrum model) is done in compliance with [TS36101], where the E-UTRA Absolute Radio Frequency Channel Number (EARFCN) is defined. The test vector for this test suite comprises a set of EARFCN values and the corresponding carrier frequency calculated by hand following the specification of [TS36101]. The test passes if the carrier frequency returned by LteSpectrumValueHelper is the same as the known value for each element in the test vector.

System Tests
Adaptive Modulation and Coding Tests

The test suite lte-link-adaptation provides system tests recreating a scenario with a single eNB and a single UE. Different test cases are created corresponding to different SNR values perceived by the UE. The aim of the test is to check that in each test case the chosen MCS corresponds to some known reference values. These reference values are obtained by re-implementing in Octave (see src/lte/test/reference/lte_amc.m) the model described in Section Adaptive Modulation and Coding for the calculation of the spectral efficiency, and determining the corresponding MCS index by manually looking up the tables in [R1-081483]. The resulting test vector is represented in Figure Test vector for Adaptive Modulation and Coding.

The MCS which is used by the simulator is measured by obtaining the tracing output produced by the scheduler after 4ms (this is needed to account for the initial delay in CQI reporting). The SINR which is calcualted by the simulator is also obtained using the LteSinrChunkProcessor interface. The test passes if both the following conditions are satisfied:

  1. the SINR calculated by the simulator correspond to the SNR of the test vector within an absolute tolerance of 10^{-7};
  2. the MCS index used by the simulator exactly corresponds to the one in the test vector.
_images/lte-mcs-index.png

Test vector for Adaptive Modulation and Coding

Inter-cell Interference Tests

The test suite lte-interference provides system tests recreating an inter-cell interference scenario with two eNBs, each having a single UE attached to it and employing Adaptive Modulation and Coding both in the downlink and in the uplink. The topology of the scenario is depicted in Figure Topology for the inter-cell interference test. The d_1 parameter represents the distance of each UE to the eNB it is attached to, whereas the d_2 parameter represent the interferer distance. We note that the scenario topology is such that the interferer distance is the same for uplink and downlink; still, the actual interference power perceived will be different, because of the different propagation loss in the uplink and downlink bands. Different test cases are obtained by varying the d_1 and d_2 parameters.

_images/lte-interference-test-scenario.png

Topology for the inter-cell interference test

The test vectors are obtained by use of a dedicated octave script (available in src/lte/test/reference/lte_link_budget_interference.m), which does the link budget calculations (including interference) corresponding to the topology of each test case, and outputs the resulting SINR and spectral efficiency. The latter is then used to determine (using the same procedure adopted for Adaptive Modulation and Coding Tests. We note that the test vector contains separate values for uplink and downlink.

UE Measurements Tests

The test suite lte-ue-measurements provides system tests recreating an inter-cell interference scenario identical of the one defined for lte-interference test-suite. However, in this test the quantities to be tested are represented by RSRP and RSRQ measurements performed by the UE in two different points of the stack: the source, which is UE PHY layer, and the destination, that is the eNB RRC.

The test vectors are obtained by the use of a dedicated octave script (available in src/lte/test/reference/lte-ue-measurements.m), which does the link budget calculations (including interference) corresponding to the topology of each test case, and outputs the resulting RSRP and RSRQ. The obtained values are then used for checking the correctness of the UE Measurements at PHY layer. After that, they have to be converted according to 3GPP formatting for the purpose of checking their correctness at eNB RRC level.

UE measurement configuration tests

Besides the previously mentioned test suite, there are 3 other test suites for testing UE measurements: lte-ue-measurements-piecewise-1, lte-ue-measurements-piecewise-2, and lte-ue-measurements-handover. These test suites are more focused on the reporting trigger procedure, i.e. the correctness of the implementation of the event-based triggering criteria is verified here.

In more specific, the tests verify the timing and the content of each measurement reports received by eNodeB. Each test case is an stand-alone LTE simulation and the test case will pass if measurement report(s) only occurs at the prescribed time and shows the correct level of RSRP (RSRQ is not verified at the moment).

Piecewise configuration

The piecewise configuration aims to test a particular UE measurements configuration. The simulation script will setup the corresponding measurements configuration to the UE, which will be active throughout the simulation.

Since the reference values are precalculated by hands, several assumptions are made to simplify the simulation. Firstly, the channel is only affected by path loss model (in this case, Friis model is used). Secondly, the ideal RRC protocol is used, and layer 3 filtering is disabled. Finally, the UE moves in a predefined motion pattern between 4 distinct spots, as depicted in Figure UE movement trace throughout the simulation in piecewise configuration below. Therefore the fluctuation of the measured RSRP can be determined more easily.

_images/ue-meas-piecewise-motion.png

UE movement trace throughout the simulation in piecewise configuration

The motivation behind the “teleport” between the predefined spots is to introduce drastic change of RSRP level, which will guarantee the triggering of entering or leaving condition of the tested event. By performing drastic changes, the test can be run within shorter amount of time.

Figure Measured RSRP trace of an example Event A1 test case in piecewise configuration below shows the measured RSRP after layer 1 filtering by the PHY layer during the simulation with a piecewise configuration. Because layer 3 filtering is disabled, these are the exact values used by the UE RRC instance to evaluate reporting trigger procedure. Notice that the values are refreshed every 200 ms, which is the default filtering period of PHY layer measurements report. The figure also shows the time when entering and leaving conditions of an example instance of Event A1 (serving cell becomes better than threshold) occur during the simulation.

_images/ue-meas-piecewise-a1.png

Measured RSRP trace of an example Event A1 test case in piecewise configuration

Each reporting criterion is tested several times with different threshold/offset parameters. Some test scenarios also take hysteresis and time-to-trigger into account. Figure Measured RSRP trace of an example Event A1 with hysteresis test case in piecewise configuration depicts the effect of hysteresis in another example of Event A1 test.

_images/ue-meas-piecewise-a1-hys.png

Measured RSRP trace of an example Event A1 with hysteresis test case in piecewise configuration

Piecewise configuration is used in two test suites of UE measurements. The first one is lte-ue-measurements-piecewise-1, henceforth Piecewise test #1, which simulates 1 UE and 1 eNodeB. The other one is lte-ue-measurements-piecewise-2, which has 1 UE and 2 eNodeBs in the simulation.

Piecewise test #1 is intended to test the event-based criteria which are not dependent on the existence of a neighbouring cell. These criteria include Event A1 and A2. The other events are also briefly tested to verify that they are still working correctly (albeit not reporting anything) in the absence of any neighbouring cell. Table UE measurements test scenarios using piecewise configuration #1 below lists the scenarios tested in piecewise test #1.

UE measurements test scenarios using piecewise configuration #1
Test # Reporting Criteria Threshold/Offset Hysteresis Time-to-Trigger
1 Event A1 Low No No
2 Event A1 Normal No No
3 Event A1 Normal No Short
4 Event A1 Normal No Long
5 Event A1 Normal No Super
6 Event A1 Normal Yes No
7 Event A1 High No No
8 Event A2 Low No No
9 Event A2 Normal No No
10 Event A2 Normal No Short
11 Event A2 Normal No Long
12 Event A2 Normal No Super
13 Event A2 Normal Yes No
14 Event A2 High No No
15 Event A3 Zero No No
16 Event A4 Normal No No
17 Event A5 Normal-Normal No No

Other events such as Event A3, A4, and A5 depend on measurements of neighbouring cell, so they are more thoroughly tested in Piecewise test #2. The simulation places the nodes on a straight line and instruct the UE to “jump” in a similar manner as in Piecewise test #1. Handover is disabled in the simulation, so the role of serving and neighbouring cells do not switch during the simulation. Table UE measurements test scenarios using piecewise configuration #2 below lists the scenarios tested in Piecewise test #2.

UE measurements test scenarios using piecewise configuration #2
Test # Reporting Criteria Threshold/Offset Hysteresis Time-to-Trigger
1 Event A1 Low No No
2 Event A1 Normal No No
3 Event A1 Normal Yes No
4 Event A1 High No No
5 Event A2 Low No No
6 Event A2 Normal No No
7 Event A2 Normal Yes No
8 Event A2 High No No
9 Event A3 Positive No No
10 Event A3 Zero No No
11 Event A3 Zero No Short
12 Event A3 Zero No Super
13 Event A3 Zero Yes No
14 Event A3 Negative No No
15 Event A4 Low No No
16 Event A4 Normal No No
17 Event A4 Normal No Short
18 Event A4 Normal No Super
19 Event A4 Normal Yes No
20 Event A4 High No No
21 Event A5 Low-Low No No
22 Event A5 Low-Normal No No
23 Event A5 Low-High No No
24 Event A5 Normal-Low No No
25 Event A5 Normal-Normal No No
26 Event A5 Normal-Normal No Short
27 Event A5 Normal-Normal No Super
28 Event A5 Normal-Normal Yes No
29 Event A5 Normal-High No No
30 Event A5 High-Low No No
31 Event A5 High-Normal No No
32 Event A5 High-High No No

One note about the tests with time-to-trigger, they are tested using 3 different values of time-to-trigger: short (shorter than report interval), long (shorter than the filter measurement period of 200 ms), and super (longer than 200 ms). The first two ensure that time-to-trigger evaluation always use the latest measurement reports received from PHY layer. While the last one is responsible for verifying time-to-trigger cancellation, for example when a measurement report from PHY shows that the entering/leaving condition is no longer true before the first trigger is fired.

Handover configuration

The purpose of the handover configuration is to verify whether UE measurement configuration is updated properly after a succesful handover takes place. For this purpose, the simulation will construct 2 eNodeBs with different UE measurement configuration, and the UE will perform handover from one cell to another. The UE will be located on a straight line between the 2 eNodeBs, and the handover will be invoked manually. The duration of each simulation is 2 seconds (except the last test case) and the handover is triggered exactly at halfway of simulation.

The lte-ue-measurements-handover test suite covers various types of configuration differences. The first one is the difference in report interval, e.g. the first eNodeB is configured with 480 ms report interval, while the second eNodeB is configured with 240 ms report interval. Therefore, when the UE performed handover to the second cell, the new report interval must take effect. As in piecewise configuration, the timing and the content of each measurement report received by the eNodeB will be verified.

Other types of differences covered by the test suite are differences in event and differences in threshold/offset. Table UE measurements test scenarios using handover configuration below lists the tested scenarios.

UE measurements test scenarios using handover configuration
Test # Test Subject Initial Configuration Post-Handover Configuration
1 Report interval 480 ms 240 ms
2 Report interval 120 ms 640 ms
3 Event Event A1 Event A2
4 Event Event A2 Event A1
5 Event Event A3 Event A4
6 Event Event A4 Event A3
7 Event Event A2 Event A3
8 Event Event A3 Event A2
9 Event Event A4 Event A5
10 Event Event A5 Event A4
11 Threshold/offset RSRP range 52 (Event A1) RSRP range 56 (Event A1)
12 Threshold/offset RSRP range 52 (Event A2) RSRP range 56 (Event A2)
13 Threshold/offset A3 offset -30 (Event A3) A3 offset +30 (Event A3)
14 Threshold/offset RSRP range 52 (Event A4) RSRP range 56 (Event A4)
15 Threshold/offset RSRP range 52-52 (Event A5) RSRP range 56-56 (Event A5)
16 Time-to-trigger 1024 ms 100 ms
17 Time-to-trigger 1024 ms 640 ms
Round Robin scheduler performance

The test suite lte-rr-ff-mac-scheduler creates different test cases with a single eNB and several UEs, all having the same Radio Bearer specification. In each test case, the UEs see the same SINR from the eNB; different test cases are implemented by using different distance among UEs and the eNB (i.e., therefore having different SINR values) and different numbers of UEs. The test consists on checking that the obtained throughput performance is equal among users and matches a reference throughput value obtained according to the SINR perceived within a given tolerance.

The test vector is obtained according to the values of transport block size reported in table 7.1.7.2.1-1 of [TS36213], considering an equal distribution of the physical resource block among the users using Resource Allocation Type 0 as defined in Section 7.1.6.1 of [TS36213]. Let \tau be the TTI duration, N be the number of UEs, B the transmission bandwidth configuration in number of RBs, G the RBG size, M the modulation and coding scheme in use at the given SINR and S(M, B) be the transport block size in bits as defined by 3GPP TS 36.213. We first calculate the number L of RBGs allocated to each user as

L = \left\lfloor \frac{B}{NG} \right\rfloor

The reference throughput T in bit/s achieved by each UE is then calculated as

T =  \frac{S(M, L G)}{8 \; \tau}

The test passes if the measured throughput matches with the reference throughput within a relative tolerance of 0.1. This tolerance is needed to account for the transient behavior at the beginning of the simulation (e.g., CQI feedback is only available after a few subframes) as well as for the accuracy of the estimator of the average throughput performance over the chosen simulation time (0.4s). This choice of the simulation time is justified by the need to follow the ns-3 guidelines of keeping the total execution time of the test suite low, in spite of the high number of test cases. In any case, we note that a lower value of the tolerance can be used when longer simulations are run.

In Figure fig-lenaThrTestCase1, the curves labeled “RR” represent the test values calculated for the RR scheduler test, as a function of the number of UEs and of the MCS index being used in each test case.

_images/lenaThrTestCase1.png

Test vectors for the RR and PF Scheduler in the downlink in a scenario where all UEs use the same MCS.

Proportional Fair scheduler performance

The test suite lte-pf-ff-mac-scheduler creates different test cases with a single eNB, using the Proportional Fair (PF) scheduler, and several UEs, all having the same Radio Bearer specification. The test cases are grouped in two categories in order to evaluate the performance both in terms of the adaptation to the channel condition and from a fairness perspective.

In the first category of test cases, the UEs are all placed at the same distance from the eNB, and hence all placed in order to have the same SINR. Different test cases are implemented by using a different SINR value and a different number of UEs. The test consists on checking that the obtained throughput performance matches with the known reference throughput up to a given tolerance. The expected behavior of the PF scheduler when all UEs have the same SNR is that each UE should get an equal fraction of the throughput obtainable by a single UE when using all the resources. We calculate the reference throughput value by dividing the throughput achievable by a single UE at the given SNR by the total number of UEs. Let \tau be the TTI duration, B the transmission bandwidth configuration in number of RBs, M the modulation and coding scheme in use at the given SINR and S(M, B) be the transport block size as defined in [TS36213]. The reference throughput T in bit/s achieved by each UE is calculated as

T = \frac{S(M,B)}{\tau N}

The curves labeled “PF” in Figure fig-lenaThrTestCase1 represent the test values calculated for the PF scheduler tests of the first category, that we just described.

The second category of tests aims at verifying the fairness of the PF scheduler in a more realistic simulation scenario where the UEs have a different SINR (constant for the whole simulation). In these conditions, the PF scheduler will give to each user a share of the system bandwidth that is proportional to the capacity achievable by a single user alone considered its SINR. In detail, let M_i be the modulation and coding scheme being used by each UE (which is a deterministic function of the SINR of the UE, and is hence known in this scenario). Based on the MCS, we determine the achievable rate R_i for each user i using the procedure described in Section~ref{sec:pfs}. We then define the achievable rate ratio \rho_{R,i} of each user i as

\rho_{R,i} = \frac{R_i}{\sum_{j=1}^N R_j}

Let now T_i be the throughput actually achieved by the UE i , which is obtained as part of the simulation output. We define the obtained throughput ratio \rho_{T,i} of UE i as

\rho_{T,i} = \frac{T_i}{\sum_{j=1}^N T_j}

The test consists of checking that the following condition is verified:

\rho_{R,i} = \rho_{T,i}

if so, it means that the throughput obtained by each UE over the whole simulation matches with the steady-state throughput expected by the PF scheduler according to the theory. This test can be derived from [Holtzman2000] as follows. From Section 3 of [Holtzman2000], we know that

\frac{T_i}{R_i} = c, \, \forall i

where c is a constant. By substituting the above into the definition of \rho_{T,i} given previously, we get

\frac{T_i}{\sum_{j=1}^N T_j} &=  \frac{c R_i}{\sum_{j=1}^N c R_j} \\
                             &=  \frac{c R_i}{c \sum_{j=1}^N  R_j} \\
                             &=  \frac{R_i}{\sum_{j=1}^N  R_j}

which is exactly the expression being used in the test.

Figure Throughput ratio evaluation for the PF scheduler in a scenario where the UEs have MCS index presents the results obtained in a test case with UEs i=1,\dots,5 that are located at a distance from the base station such that they will use respectively the MCS index 28, 24, 16, 12,
6. From the figure, we note that, as expected, the obtained throughput is proportional to the achievable rate. In other words, the PF scheduler assign more resources to the users that use a higher MCS index.

_images/lenaThrTestCase2.png

Throughput ratio evaluation for the PF scheduler in a scenario where the UEs have MCS index 28, 24, 16, 12, 6

Maximum Throughput scheduler performance

Test suites lte-fdmt-ff-mac-scheduler and lte-tdmt-ff-mac-scheduler create different test cases with a single eNB and several UEs, all having the same Radio Bearer specification, using the Frequency Domain Maximum Throughput (FDMT) scheduler and Time Domain Maximum Throughput (TDMT) scheduler respectively. In other words, UEs are all placed at the same distance from the eNB, and hence all placed in order to have the same SNR. Different test cases are implemented by using a different SNR values and a different number of UEs. The test consists on checking that the obtained throughput performance matches with the known reference throughput up to a given tolerance.The expected behavior of both FDMT and TDMT scheduler when all UEs have the same SNR is that scheduler allocates all RBGs to the first UE defined in script. This is because the current FDMT and TDMT implementation always select the first UE to serve when there are multiple UEs having the same SNR value. We calculate the reference throughput value for first UE by the throughput achievable of a single UE at the given SNR, while reference throughput value for other UEs by zero. Let \tau be the TTI duration, B the transmission bandwidth configuration in number of RBs, M the modulation and coding scheme in use at the given SNR and S(M, B) be the transport block size as defined in [TS36213]. The reference throughput T in bit/s achieved by each UE is calculated as

T = \frac{S(M,B)}{\tau}

Throughput to Average scheduler performance

Test suites lte-tta-ff-mac-scheduler create different test cases with a single eNB and several UEs, all having the same Radio Bearer specification using TTA scheduler. Network topology and configurations in TTA test case are as the same as the test for MT scheduler. More complex test case needs to be developed to show the fairness feature of TTA scheduler.

Blind Average Throughput scheduler performance

Test suites lte-tdbet-ff-mac-scheduler and lte-fdbet-ff-mac-scheduler create different test cases with a single eNB and several UEs, all having the same Radio Bearer specification.

In the first test case of lte-tdbet-ff-mac-scheduler and lte-fdbet-ff-mac-scheduler, the UEs are all placed at the same distance from the eNB, and hence all placed in order to have the same SNR. Different test cases are implemented by using a different SNR value and a different number of UEs. The test consists on checking that the obtained throughput performance matches with the known reference throughput up to a given tolerance. In long term, the expected behavior of both TD-BET and FD-BET when all UEs have the same SNR is that each UE should get an equal throughput. However, the exact throughput value of TD-BET and FD-BET in this test case is not the same.

When all UEs have the same SNR, TD-BET can be seen as a specific case of PF where achievable rate equals to 1. Therefore, the throughput obtained by TD-BET is equal to that of PF. On the other hand, FD-BET performs very similar to the round robin (RR) scheduler in case of that all UEs have the same SNR and the number of UE( or RBG) is an integer multiple of the number of RBG( or UE). In this case, FD-BET always allocate the same number of RBGs to each UE. For example, if eNB has 12 RBGs and there are 6 UEs, then each UE will get 2 RBGs in each TTI. Or if eNB has 12 RBGs and there are 24 UEs, then each UE will get 2 RBGs per two TTIs. When the number of UE (RBG) is not an integer multiple of the number of RBG (UE), FD-BET will not follow the RR behavior because it will assigned different number of RBGs to some UEs, while the throughput of each UE is still the same.

The second category of tests aims at verifying the fairness of the both TD-BET and FD-BET schedulers in a more realistic simulation scenario where the UEs have a different SNR (constant for the whole simulation). In this case, both scheduler should give the same amount of averaged throughput to each user.

Specifically, for TD-BET, let F_i be the fraction of time allocated to user i in total simulation time, R^{fb}_i be the the full bandwidth achievable rate for user i and T_i be the achieved throughput of user i. Then we have:

T_i = F_i R^{fb}_i

In TD-BET, the sum of F_i for all user equals one. In long term, all UE has the same T_i so that we replace T_i by T. Then we have:

T = \frac{1}{ \sum_{i=1}^{N} \frac{1}{R^{fb}_i} }

Token Band Fair Queue scheduler performance

Test suites lte-fdtbfq-ff-mac-scheduler and lte-tdtbfq-ff-mac-scheduler create different test cases for testing three key features of TBFQ scheduler: traffic policing, fairness and traffic balance. Constant Bit Rate UDP traffic is used in both downlink and uplink in all test cases. The packet interval is set to 1ms to keep the RLC buffer non-empty. Different traffic rate is achieved by setting different packet size. Specifically, two classes of flows are created in the testsuites:

  • Homogeneous flow: flows with the same token generation rate and packet arrival rate
  • Heterogeneous flow: flows with different packet arrival rate, but with the same token generation rate

In test case 1 verifies traffic policing and fairness features for the scenario that all UEs are placed at the same distance from the eNB. In this case, all Ues have the same SNR value. Different test cases are implemented by using a different SNR value and a different number of UEs. Because each flow have the same traffic rate and token generation rate, TBFQ scheduler will guarantee the same throughput among UEs without the constraint of token generation rate. In addition, the exact value of UE throughput is depended on the total traffic rate:

  • If total traffic rate <= maximum throughput, UE throughput = traffic rate
  • If total traffic rate > maximum throughput, UE throughput = maximum throughput / N

Here, N is the number of UE connected to eNodeB. The maximum throughput in this case equals to the rate that all RBGs are assigned to one UE(e.g., when distance equals 0, maximum throughput is 2196000 byte/sec). When the traffic rate is smaller than max bandwidth, TBFQ can police the traffic by token generation rate so that the UE throughput equals its actual traffic rate (token generation rate is set to traffic generation rate); On the other hand, when total traffic rate is bigger than the max throughput, eNodeB cannot forward all traffic to UEs. Therefore, in each TTI, TBFQ will allocate all RBGs to one UE due to the large packets buffered in RLC buffer. When a UE is scheduled in current TTI, its token counter is decreased so that it will not be scheduled in the next TTI. Because each UE has the same traffic generation rate, TBFQ will serve each UE in turn and only serve one UE in each TTI (both in TD TBFQ and FD TBFQ). Therefore, the UE throughput in the second condition equals to the evenly share of maximum throughput.

Test case 2 verifies traffic policing and fairness features for the scenario that each UE is placed at the different distance from the eNB. In this case, each UE has the different SNR value. Similar to test case 1, UE throughput in test case 2 is also depended on the total traffic rate but with a different maximum throughput. Suppose all UEs have a high traffic load. Then the traffic will saturate the RLC buffer in eNodeB. In each TTI, after selecting one UE with highest metric, TBFQ will allocate all RBGs to this UE due to the large RLC buffer size. On the other hand, once RLC buffer is saturated, the total throughput of all UEs cannot increase any more. In addition, as we discussed in test case 1, for homogeneous flows which have the same t_i and r_i, each UE will achieve the same throughput in long term. Therefore, we can use the same method in TD BET to calculate the maximum throughput:

T = \frac{N}{ \sum_{i=1}^{N} \frac{1}{R^{fb}_i} }

Here, T is the maximum throughput. R^{fb}_i be the the full bandwidth achievable rate for user i. N is the number of UE.

When the totol traffic rate is bigger than T, the UE throughput equals to \frac{T}{N} . Otherwise, UE throughput equals to its traffic generation rate.

In test case 3, three flows with different traffic rate are created. Token generation rate for each flow is the same and equals to the average traffic rate of three flows. Because TBFQ use a shared token bank, tokens contributed by UE with lower traffic load can be utilized by UE with higher traffic load. In this way, TBFQ can guarantee the traffic rate for each flow. Although we use heterogeneous flow here, the calculation of maximum throughput is as same as that in test case 2. In calculation max throughput of test case 2, we assume that all UEs suffer high traffic load so that scheduler always assign all RBGs to one UE in each TTI. This assumes is also true in heterogeneous flow case. In other words, whether those flows have the same traffic rate and token generation rate, if their traffic rate is bigger enough, TBFQ performs as same as it in test case 2. Therefore, the maximum bandwidth in test case 3 is as same as it in test case 2.

In test case 3, in some flows, token generate rate does not equal to MBR, although all flows are CBR traffic. This is not accorded with our parameter setting rules. Actually, the traffic balance feature is used in VBR traffic. Because different UE’s peak rate may occur in different time, TBFQ use shared token bank to balance the traffic among those VBR traffics. Test case 3 use CBR traffic to verify this feature. But in the real simulation, it is recommended to set token generation rate to MBR.

Priority Set scheduler performance

Test suites lte-pss-ff-mac-scheduler create different test cases with a single eNB and several UEs. In all test cases, we select PFsch in FD scheduler. Same testing results can also be obtained by using CoItA scheduler. In addition, all test cases do not define nMux so that TD scheduler in PSS will always select half of total UE.

In the first class test case of lte-pss-ff-mac-scheduler, the UEs are all placed at the same distance from the eNB, and hence all placed in order to have the same SNR. Different test cases are implemented by using a different TBR for each UEs. In each test cases, all UEs have the same Target Bit Rate configured by GBR in EPS bear setting. The expected behavior of PSS is to guarantee that each UE’s throughput at least equals its TBR if the total flow rate is blow maximum throughput. Similar to TBFQ, the maximum throughput in this case equals to the rate that all RBGs are assigned to one UE. When the traffic rate is smaller than max bandwidth, the UE throughput equals its actual traffic rate; On the other hand, UE throughput equals to the evenly share of the maximum throughput.

In the first class of test cases, each UE has the same SNR. Therefore, the priority metric in PF scheduler will be determined by past average throughput T_{j}(t) because each UE has the same achievable throughput R_{j}(k,t) in PFsch or same CoI[k,n] in CoItA. This means that PSS will performs like a TD-BET which allocates all RBGs to one UE in each TTI. Then the maximum value of UE throughput equals to the achievable rate that all RBGs are allocated to this UE.

In the second class of test case of lte-pss-ff-mac-scheduler, the UEs are all placed at the same distance from the eNB, and hence all placed in order to have the same SNR. Different TBR values are assigned to each UE. There also exist an maximum throughput in this case. Once total traffic rate is bigger than this threshold, there will be some UEs that cannot achieve their TBR. Because there is no fading, subband CQIs for each RBGs frequency are the same. Therefore, in FD scheduler,in each TTI, priority metrics of UE for all RBGs are the same. This means that FD scheduler will always allocate all RBGs to one user. Therefore, in the maximum throughput case, PSS performs like a TD-BET. Then we have:

T = \frac{N}{ \sum_{i=1}^N \frac{1}{R^{fb}_i} }

Here, T is the maximum throughput. R^{fb}_i be the the full bandwidth achievable rate for user i. N is the number of UE.

Channel and QoS aware scheduler performance

The performance of the Channel and QoS aware scheduler can be tested in the similar way to performance of Priority Set scheduler when GBR flows are not delay sensitive by measuring if the achieved throughput at RLC layer is close to the TBR. Having this in mind, the performance of the CQA scheduler is tested by using the same test cases as the lte-pss-ff-mac-scheduler. Additionally, in [Bbojovic2014] can be found performance evaluation of CQA scheduler when the GBR flows are delay sensitive by considering different QoE metrics.

Building Propagation Loss Model

The aim of the system test is to verify the integration of the BuildingPathlossModel with the lte module. The test exploits a set of three pre calculated losses for generating the expected SINR at the receiver counting the transmission and the noise powers. These SINR values are compared with the results obtained from a LTE simulation that uses the BuildingPathlossModel. The reference loss values are calculated off-line with an Octave script (/test/reference/lte_pathloss.m). Each test case passes if the reference loss value is equal to the value calculated by the simulator within a tolerance of 0.001 dB, which accouns for numerical errors in the calculations.

Physical Error Model

The test suite lte-phy-error-model generates different test cases for evaluating both data and control error models. For what concern the data, the test consists of six test cases with single eNB and a various number of UEs, all having the same Radio Bearer specification. Each test is designed for evaluating the error rate perceived by a specific TB size in order to verify that it corresponds to the expected values according to the BLER generated for CB size analog to the TB size. This means that, for instance, the test will check that the performance of a TB of N bits is analogous to the one of a CB size of N bits by collecting the performance of a user which has been forced the generation of a such TB size according to the distance to eNB. In order to significantly test the BLER at MAC level, we configured the Adaptive Modulation and Coding (AMC) module, the LteAmc class, for making it less robust to channel conditions by using the PiroEW2010 AMC model and configuring it to select the MCS considering a target BER of 0.03 (instead of the default value of 0.00005). We note that these values do not reflect the actual BER, since they come from an analytical bound which does not consider all the transmission chain aspects; therefore the BER and BLER actually experienced at the reception of a TB is in general different.

The parameters of the six test cases are reported in the following:

  1. 4 UEs placed 1800 meters far from the eNB, which implies the use of MCS 2 (SINR of -5.51 dB) and a TB of 256 bits, that in turns produce a BLER of 0.33 (see point A in figure BLER for tests 1, 2, 3.).
  2. 2 UEs placed 1800 meters far from the eNB, which implies the use of MCS 2 (SINR of -5.51 dB) and a TB of 528 bits, that in turns produce a BLER of 0.11 (see point B in figure BLER for tests 1, 2, 3.).
  3. 1 UE placed 1800 meters far from the eNB, which implies the use of MCS 2 (SINR of -5.51 dB) and a TB of 1088 bits, that in turns produce a BLER of 0.02 (see point C in figure BLER for tests 1, 2, 3.).
  4. 1 UE placed 600 meters far from the eNB, which implies the use of MCS 12 (SINR of 4.43 dB) and a TB of 4800 bits, that in turns produce a BLER of 0.3 (see point D in figure BLER for tests 4, 5.).
  5. 3 UEs placed 600 meters far from the eNB, which implies the use of MCS 12 (SINR of 4.43 dB) and a TB of 1632 bits, that in turns produce a BLER of 0.55 (see point E in figure BLER for tests 4, 5.).
  6. 1 UE placed 470 meters far from the eNB, which implies the use of MCS 16 (SINR of 8.48 dB) and a TB of 7272 bits (segmented in 2 CBs of 3648 and 3584 bits), that in turns produce a BLER of 0.14, since each CB has CBLER equal to 0.075 (see point F in figure BLER for test 6.).
_images/MCS_2_test.png

BLER for tests 1, 2, 3.

_images/MCS_12_test.png

BLER for tests 4, 5.

_images/MCS_16_test.png

BLER for test 6.

The test condition verifies that in each test case the expected number of packets received correctly corresponds to a Bernoulli distribution with a confidence interval of 99%, where the probability of success in each trail is p=1-BER and n is the total number of packets sent.

The error model of PCFICH-PDCCH channels consists of 4 test cases with a single UE and several eNBs, where the UE is connected to only one eNB in order to have the remaining acting as interfering ones. The errors on data are disabled in order to verify only the ones due to erroneous decodification of PCFICH-PDCCH. As before, the system has been forced on working in a less conservative fashion in the AMC module for appreciating the results in border situations. The parameters of the 4 tests cases are reported in the following:

  1. 2 eNBs placed 1078 meters far from the UE, which implies a SINR of -2.00 dB and a TB of 217 bits, that in turns produce a BLER of 0.007.
  2. 3 eNBs placed 1040 meters far from the UE, which implies a SINR of -4.00 dB and a TB of 217 bits, that in turns produce a BLER of 0.045.
  3. 4 eNBs placed 1250 meters far from the UE, which implies a SINR of -6.00 dB and a TB of 133 bits, that in turns produce a BLER of 0.206.
  4. 5 eNBs placed 1260 meters far from the UE, which implies a SINR of -7.00 dB and a TB of 81 bits, that in turns produce a BLER of 0.343.

The test condition verifies that in each test case the expected number of packets received correct corresponds to a Bernoulli distribution with a confidence interval of 99.8%, where the probability of success in each trail is p=1-BER and n is the total number of packet sent. The larger confidence interval is due to the errors that might be produced in quantizing the MI and the error curve.

HARQ Model

The test suite lte-harq includes two tests for evaluating the HARQ model and the related extension in the error model. The test consists on checking whether the amount of bytes received during the simulation corresponds to the expected ones according to the values of transport block and the HARQ dynamics. In detail, the test checks whether the throughput obtained after one HARQ retransmission is the expeted one. For evaluating the expected throughput the expected TB delivering time has been evaluated according to the following formula:

\mathrm{T} = P_s^1 \times 1 + P_s^2 \times 2 + (1-P_s^2) \times 3

where P_s^i is the probability of receiving with success the HARQ block at the attempt i (i.e., the RV with 3GPP naming). According to the scenarios, in the test we always have P_s^1 equal to 0.0, while P_s^2 varies in the two tests, in detail:

\mathrm{T_{test-1}} = 0.0 \times 1 + 0.926 \times 2 + 0.074 \times 3 = 2.074

\mathrm{T_{test-2}} = 0.0 \times 1 + 0.752 \times 2 + 0.248 \times 3 = 2.248

The expected throughput is calculted by counting the number of transmission slots available during the simulation (e.g., the number of TTIs) and the size of the TB in the simulation, in detail:

\mathrm{Thr_{test-i}} = \frac{TTI_{NUM}}{T_{test-i}} TB_{size} = \left\{ \begin{array}{lll} \dfrac{1000}{2.074}66 = 31822\mbox{ bps} & \mbox{ for test-1} \\ & \\ \dfrac{1000}{2.248}472 = 209964\mbox{ bps} & \mbox{ for test-2}\end{array} \right.

where TTI_{NUM} is the total number of TTIs in 1 second. The test is performed both for Round Robin scheduler. The test passes if the measured throughput matches with the reference throughput within a relative tolerance of 0.1. This tolerance is needed to account for the transient behavior at the beginning of the simulation and the on-fly blocks at the end of the simulation.

MIMO Model

The test suite lte-mimo aims at verifying both the effect of the gain considered for each Transmission Mode on the system performance and the Transmission Mode switching through the scheduler interface. The test consists on checking whether the amount of bytes received during a certain window of time (0.1 seconds in our case) corresponds to the expected ones according to the values of transport block size reported in table 7.1.7.2.1-1 of [TS36213], similarly to what done for the tests of the schedulers.

The test is performed both for Round Robin and Proportional Fair schedulers. The test passes if the measured throughput matches with the reference throughput within a relative tolerance of 0.1. This tolerance is needed to account for the transient behavior at the beginning of the simulation and the transition phase between the Transmission Modes.

Antenna Model integration

The test suite lte-antenna checks that the AntennaModel integrated with the LTE model works correctly. This test suite recreates a simulation scenario with one eNB node at coordinates (0,0,0) and one UE node at coordinates (x,y,0). The eNB node is configured with an CosineAntennaModel having given orientation and beamwidth. The UE instead uses the default IsotropicAntennaModel. The test checks that the received power both in uplink and downlink account for the correct value of the antenna gain, which is determined offline; this is implemented by comparing the uplink and downlink SINR and checking that both match with the reference value up to a tolerance of 10^{-6} which accounts for numerical errors. Different test cases are provided by varying the x and y coordinates of the UE, and the beamwidth and the orientation of the antenna of the eNB.

RLC

Two test suites lte-rlc-um-transmitter and lte-rlc-am-transmitter check that the UM RLC and the AM RLC implementation work correctly. Both these suites work by testing RLC instances connected to special test entities that play the role of the MAC and of the PDCP, implementing respectively the LteMacSapProvider and LteRlcSapUser interfaces. Different test cases (i.e., input test vector consisting of series of primitive calls by the MAC and the PDCP) are provided that check the behavior in the following cases:

  1. one SDU, one PDU: the MAC notifies a TX opportunity causes the creation of a PDU which exactly contains a whole SDU
  2. segmentation: the MAC notifies multiple TX opportunities that are smaller than the SDU size stored in the transmission buffer, which is then to be fragmented and hence multiple PDUs are generated;
  3. concatenation: the MAC notifies a TX opportunity that is bigger than the SDU, hence multiple SDUs are concatenated in the same PDU
  4. buffer status report: a series of new SDUs notifications by the PDCP is inteleaved with a series of TX opportunity notification in order to verify that the buffer status report procedure is correct.

In all these cases, an output test vector is determine manually from knowledge of the input test vector and knowledge of the expected behavior. These test vector are specialized for UM RLC and AM RLC due to their different behavior. Each test case passes if the sequence of primitives triggered by the RLC instance being tested is exacly equal to the output test vector. In particular, for each PDU transmitted by the RLC instance, both the size and the content of the PDU are verified to check for an exact match with the test vector.

RRC

The test suite lte-rrc tests the correct functionality of the following aspects:

  1. MAC Random Access
  2. RRC System Information Acquisition
  3. RRC Connection Establishment
  4. RRC Reconfiguration

The test suite considers a type of scenario with a single eNB and multiple UEs that are instructed to connect to the eNB. Each test case implement an instance of this scenario with specific values of the following parameters:

  • number of UEs
  • number of Data Radio Bearers to be activated for each UE
  • time t^c_0 at which the first UE is instructed to start connecting to the eNB
  • time interval d^i between the start of connection of UE n and UE n+1; the time at which user n connects is thus determined as t^c_n = t^c_0 + n d^i sdf
  • a boolean flag indicating whether the ideal or the real RRC protocol model is used

Each test cases passes if a number of test conditions are positively evaluated for each UE after a delay d^e from the time it started connecting to the eNB. The delay d^e is determined as

d^e = d^{si} + d^{ra} + d^{ce} + d^{cr}

where:

  • d^{si} is the max delay necessary for the acquisition of System Information. We set it to 90ms accounting for 10ms for the MIB acquisition and 80ms for the subsequent SIB2 acquisition
  • d^{ra} is the delay for the MAC Random Access (RA) procedure. This depends on preamble collisions as well as on the availability of resources for the UL grant allocation. The total amount of necessary RA attempts depends on preamble collisions and failures to allocate the UL grant because of lack of resources. The number of collisions depends on the number of UEs that try to access simultaneously; we estimated that for a 0.99 RA success probability, 5 attempts are sufficient for up to 20 UEs, and 10 attempts for up to 50 UEs. For the UL grant, considered the system bandwidth and the default MCS used for the UL grant (MCS 0), at most 4 UL grants can be assigned in a TTI; so for n UEs trying to do RA simultaneously the max number of attempts due to the UL grant issue is \lceil n/4 \rceil. The time for a RA attempt is determined by 3ms + the value of LteEnbMac::RaResponseWindowSize, which defaults to 3ms, plus 1ms for the scheduling of the new transmission.
  • d^{ce} is the delay required for the transmission of RRC CONNECTION SETUP + RRC CONNECTION SETUP COMPLETED. We consider a round trip delay of 10ms plus \lceil 2n/4 \rceil considering that 2 RRC packets have to be transmitted and that at most 4 such packets can be transmitted per TTI.
  • d^{cr} is the delay required for eventually needed RRC CONNECTION RECONFIGURATION transactions. The number of transactions needed is 1 for each bearer activation. Similarly to what done for d^{ce}, for each transaction we consider a round trip delay of 10ms plus \lceil 2n/4 \rceil. delay of 20ms.

The conditions that are evaluated for a test case to pass are, for each UE:

  • the eNB has the context of the UE (identified by the RNTI value retrieved from the UE RRC)
  • the RRC state of the UE at the eNB is CONNECTED_NORMALLY
  • the RRC state at the UE is CONNECTED_NORMALLY
  • the UE is configured with the CellId, DlBandwidth, UlBandwidth, DlEarfcn and UlEarfcn of the eNB
  • the IMSI of the UE stored at the eNB is correct
  • the number of active Data Radio Bearers is the expected one, both at the eNB and at the UE
  • for each Data Radio Bearer, the following identifiers match between the UE and the eNB: EPS bearer id, DRB id, LCID
Initial cell selection

The test suite lte-cell-selection is responsible for verifying the Initial Cell Selection procedure. The test is a simulation of a small network of 2 non-CSG cells and 2 non-CSG cells. Several static UEs are then placed at predefined locations. The UEs enter the simulation without being attached to any cell. Initial cell selection is enabled for these UEs, so each UE will find the best cell and attach to it by themselves.

At predefined check points time during the simulation, the test verifies that every UE is attached to the right cell. Moreover, the test also ensures that the UE is properly connected, i.e., its final state is CONNECTED_NORMALLY. Figure Sample result of cell selection test depicts the network layout and the expected result. When a UE is depicted as having 2 successful cell selections (e.g., UE #3 and #4), any of them is accepted by the test case.

_images/lte-cell-selection-scenario.png

Sample result of cell selection test

The figure shows that CSG members may attach to either CSG or non-CSG cells, and simply choose the stronger one. On the other hand, non-members can only attach to non-CSG cells, even when they are actually receiving stronger signal from a CSG cell.

For reference purpose, Table UE error rate in Initial Cell Selection test shows the error rate of each UE when receiving transmission from the control channel. Based on this information, the check point time for UE #3 is done at a later time than the others to compensate for its higher risk of failure.

UE error rate in Initial Cell Selection test
UE # Error rate
1 0.00%
2 1.44%
3 12.39%
4 0.33%
5 0.00%
6 0.00%

The test uses the default Friis path loss model and without any channel fading model.

GTP-U protocol

The unit test suite epc-gtpu checks that the encoding and decoding of the GTP-U header is done correctly. The test fills in a header with a set of known values, adds the header to a packet, and then removes the header from the packet. The test fails if, upon removing, any of the fields in the GTP-U header is not decoded correctly. This is detected by comparing the decoded value from the known value.

S1-U interface

Two test suites (epc-s1u-uplink and epc-s1u-downlink) make sure that the S1-U interface implementation works correctly in isolation. This is achieved by creating a set of simulation scenarios where the EPC model alone is used, without the LTE model (i.e., without the LTE radio protocol stack, which is replaced by simple CSMA devices). This checks that the interoperation between multiple EpcEnbApplication instances in multiple eNBs and the EpcSgwPgwApplication instance in the SGW/PGW node works correctly in a variety of scenarios, with varying numbers of end users (nodes with a CSMA device installed), eNBs, and different traffic patterns (packet sizes and number of total packets). Each test case works by injecting the chosen traffic pattern in the network (at the considered UE or at the remote host for in the uplink or the downlink test suite respectively) and checking that at the receiver (the remote host or each considered UE, respectively) that exactly the same traffic patterns is received. If any mismatch in the transmitted and received traffic pattern is detected for any UE, the test fails.

TFT classifier

The test suite epc-tft-classifier checks in isolation that the behavior of the EpcTftClassifier class is correct. This is performed by creating different classifier instances where different TFT instances are activated, and testing for each classifier that an heterogeneous set of packets (including IP and TCP/UDP headers) is classified correctly. Several test cases are provided that check the different matching aspects of a TFT (e.g. local/remote IP address, local/remote port) both for uplink and downlink traffic. Each test case corresponds to a specific packet and a specific classifier instance with a given set of TFTs. The test case passes if the bearer identifier returned by the classifier exactly matches with the one that is expected for the considered packet.

End-to-end LTE-EPC data plane functionality

The test suite lte-epc-e2e-data ensures the correct end-to-end functionality of the LTE-EPC data plane. For each test case in this suite, a complete LTE-EPC simulation scenario is created with the following characteristics:

  • a given number of eNBs
  • for each eNB, a given number of UEs
  • for each UE, a given number of active EPS bearers
  • for each active EPS bearer, a given traffic pattern (number of UDP packets to be transmitted and packet size)

Each test is executed by transmitting the given traffic pattern both in the uplink and in the downlink, at subsequent time intervals. The test passes if all the following conditions are satisfied:

  • for each active EPS bearer, the transmitted and received traffic pattern (respectively at the UE and the remote host for uplink, and vice versa for downlink) is exactly the same
  • for each active EPS bearer and each direction (uplink or downlink), exactly the expected number of packet flows over the corresponding RadioBearer instance
X2 handover

The test suite lte-x2-handover checks the correct functionality of the X2 handover procedure. The scenario being tested is a topology with two eNBs connected by an X2 interface. Each test case is a particular instance of this scenario defined by the following parameters:

  • the number of UEs that are initially attached to the first eNB
  • the number of EPS bearers activated for each UE
  • a list of handover events to be triggered, where each event is defined by: + the start time of the handover trigger + the index of the UE doing the handover + the index of the source eNB + the index of the target eNB
  • a boolean flag indicating whether the target eNB admits the handover or not
  • a boolean flag indicating whether the ideal RRC protocol is to be used instead of the real RRC protocol
  • the type of scheduler to be used (RR or PF)

Each test case passes if the following conditions are true:

  • at time 0.06s, the test CheckConnected verifies that each UE is connected to the first eNB
  • for each event in the handover list:
    • at the indicated event start time, the indicated UE is connected to the indicated source eNB
    • 0.1s after the start time, the indicated UE is connected to the indicated target eNB
    • 0.6s after the start time, for each active EPS bearer, the uplink and downlink sink applications of the indicated UE have achieved a number of bytes which is at least half the number of bytes transmitted by the corresponding source applications

The condition “UE is connected to eNB” is evaluated positively if and only if all the following conditions are met:

  • the eNB has the context of the UE (identified by the RNTI value retrieved from the UE RRC)
  • the RRC state of the UE at the eNB is CONNECTED_NORMALLY
  • the RRC state at the UE is CONNECTED_NORMALLY
  • the UE is configured with the CellId, DlBandwidth, UlBandwidth, DlEarfcn and UlEarfcn of the eNB
  • the IMSI of the UE stored at the eNB is correct
  • the number of active Data Radio Bearers is the expected one, both at the eNB and at the UE
  • for each Data Radio Bearer, the following identifiers match between the UE and the eNB: EPS bearer id, DRB id, LCID
Automatic X2 handover

The test suite lte-x2-handover-measures checks the correct functionality of the handover algorithm. The scenario being tested is a topology with two, three or four eNBs connected by an X2 interface. The eNBs are located in a straight line in the X-axes. A UE moves along the X-axes going from the neighbourhood of one eNB to the next eNB. Each test case is a particular instance of this scenario defined by the following parameters:

  • the number of eNBs in the X-axes
  • the number of UEs
  • the number of EPS bearers activated for the UE
  • a list of check point events to be triggered, where each event is defined by: + the time of the first check point event + the time of the last check point event + interval time between two check point events + the index of the UE doing the handover + the index of the eNB where the UE must be connected
  • a boolean flag indicating whether UDP traffic is to be used instead of TCP traffic
  • the type of scheduler to be used
  • the type of handover algorithm to be used
  • a boolean flag indicating whether handover is admitted by default
  • a boolean flag indicating whether the ideal RRC protocol is to be used instead of the real RRC protocol

The test suite consists of many test cases. In fact, it has been one of the most time-consuming test suite in ns-3. The test cases run with some combination of the following variable parameters:

Each test case passes if the following conditions are true:

  • at time 0.08s, the test CheckConnected verifies that each UE is connected to the first eNB
  • for each event in the check point list:
    • at the indicated check point time, the indicated UE is connected to the indicated eNB
    • 0.5s after the check point, for each active EPS bearer, the uplink and downlink sink applications of the UE have achieved a number of bytes which is at least half the number of bytes transmitted by the corresponding source applications

The condition “UE is connected to eNB” is evaluated positively if and only if all the following conditions are met:

  • the eNB has the context of the UE (identified by the RNTI value retrieved from the UE RRC)
  • the RRC state of the UE at the eNB is CONNECTED_NORMALLY
  • the RRC state at the UE is CONNECTED_NORMALLY
  • the UE is configured with the CellId, DlBandwidth, UlBandwidth, DlEarfcn and UlEarfcn of the eNB
  • the IMSI of the UE stored at the eNB is correct
  • the number of active Data Radio Bearers is the expected one, both at the eNB and at the UE
  • for each Data Radio Bearer, the following identifiers match between the UE and the eNB: EPS bearer id, DRB id, LCID
Handover delays

Handover procedure consists of several message exchanges between UE, source eNodeB, and target eNodeB over both RRC protocol and X2 interface. Test suite lte-handover-delay verifies that this procedure consistently spends the same amount of time.

The test suite will run several handover test cases. Eact test case is an individual simulation featuring a handover at a specified time in simulation. For example, the handover in the first test case is invoked at time +0.100s, while in the second test case it is at +0.101s. There are 10 test cases, each testing a different subframe in LTE. Thus the last test case has the handover at +0.109s.

The simulation scenario in the test cases is as follow:

  • EPC is enabled
  • 2 eNodeBs with circular (isotropic) antenna, separated by 1000 meters
  • 1 static UE positioned exactly in the center between the eNodeBs
  • no application installed
  • no channel fading
  • default path loss model (Friis)
  • 0.300s simulation duration

The test case runs as follow. At the beginning of the simulation, the UE is attached to the first eNodeB. Then at the time specified by the test case input argument, a handover request will be explicitly issued to the second eNodeB. The test case will then record the starting time, wait until the handover is completed, and then record the completion time. If the difference between the completion time and starting time is less than a predefined threshold, then the test passes.

A typical handover in the current ns-3 implementation takes 4.2141 ms when using Ideal RRC protocol model, and 19.9283 ms when using Real RRC protocol model. Accordingly, the test cases use 5 ms and 20 ms as the maximum threshold values. The test suite runs 10 test cases with Ideal RRC protocol model and 10 test cases with Real RRC protocol model. More information regarding these models can be found in Section RRC protocol models.

The motivation behind using subframes as the main test parameters is the fact that subframe index is one of the factors for calculating RA-RNTI, which is used by Random Access during the handover procedure. The test cases verify this computation, utilizing the fact that the handover will be delayed when this computation is broken. In the default simulation configuration, the handover delay observed because of a broken RA-RNTI computation is typically 6 ms.

Selection of target cell in handover algorithm

eNodeB may utilize Handover algorithm to automatically create handover decisions during simulation. The decision includes the UE which should do the handover and the target cell where the UE should perform handover to.

The test suite lte-handover-target verifies that the handover algorithm is making the right decision, in particular, in choosing the right target cell. It consists of several short test cases for different network topology (2×2 grid and 3×2 grid) and types of handover algorithm (the A2-A4-RSRQ handover algorithm and the strongest cell handover algorithm).

Each test case is a simulation of a micro-cell environment with the following parameter:

  • EPC is enabled
  • several circular (isotropic antenna) micro-cell eNodeBs in a rectangular grid layout, with 130 m distance between each adjacent point
  • 1 static UE, positioned close to and attached to the source cell
  • no control channel error model
  • no application installed
  • no channel fading
  • default path loss model (Friis)
  • 1s simulation duration

To trigger a handover, the test case “shutdowns” the source cell at +0.5s simulation time. Figure lte-handover-target test scenario in a 2×2 grid below illustrates the process. This is done by setting the source cell’s Tx power to a very low value. As a result, the handover algorithm notices that the UE deserves a handover and several neighbouring cells become candidates of target cell at the same time.

_images/lte-handover-target-scenario.png

lte-handover-target test scenario in a 2×2 grid

The test case then verifies that the handover algorithm, when faced with more than one options of target cells, is able to choose the right one.

Profiling Documentation

Overview and objectives

The main objective of the profiling carried out is to assess the simulator performance on a broad set of scenarios. This evaluation provides reference values for simulation running times and memory consumption figures. It also helps to identify potential performance improvements and to check for scalability problems when increasing the number of eNodeB and UEs attached to those.

In the following sections, a detailed description of the general profiling framework employed to perform the study is introduced. It also includes details on the main performed tests and its results evaluation.

Framework description

Simulation scripts

The simulation script used for all the E-UTRAN results showed in this documentation is located at src/lte/examples/lena-profiling.cc. It uses the complete PHY and MAC UE/eNodeB implementation with a simplified RLC implementation on top. This script generates a squared grid topology, placing a eNodeB at the centre of each square. UEs attached to this node are scattered randomly across the square (using a random uniform distribution along X and Y axis). If BuildingPropagationModel is used, the squares are replaced by rooms. To generate the UL and DL traffic, the RLC implementation always report data to be transfered.

_images/eutran-profiling-scenario.png

E-UTRAN

For the EPC results, the script is src/lte/examples/lena-simple-epc.cc. It uses a complete E-UTRAN implementation (PHY+MAC+RLC/UM+PDCP) and the most relevant EPC user plane entities the PGW and SGW, including GTP-U tunneling. This script generates a given number of eNodeBs, distributed across a line and attaches a single UE to every eNodeB. It also creates an EPC network and an external host connected to it through the Internet. Each UE sends and receives data to and from the remote host. In addition, each UE is also sending data to the UE camped in the adjacent eNodeB.

_images/epc-profiling-scenario.png

Propagation Model

RLC and MAC traces are enabled for all UEs and all eNodeBs and those traces are written to disk directly. The MAC scheduler used is round robin.

Simulation input parameters

The lena-profiling simulation script accepts the following input parameters:

  • simTime: time to simulate (in seconds)
  • nUe: number of UEs attached to each eNodeB
  • nEnb: number of eNodeB composing the grid per floor
  • nFloors: number of floors, 0 for Friis propagation model (no walls), 1 or greater for Building propagation model generating a nFloors-storey building.
  • traceDirectory: destination directory where simulation traces will be stored

The lena-simple-epc script accepts those other parameters:

  • simTime: time to simulate (in seconds)
  • numberOfNodes: number of eNodeB + UE pairs created
Time measurement

Running time is measured using default Linux shell command time. This command counts how much user time the execution of a program takes.

Perl script

To simplify the process of running the profiling script for a wide range of values and collecting its timing data, a simple Perl script to automate the complete process is provided. It is placed in src/lte/test/lte-test-run-time.pl for lena-profiling and in src/lte/epc-test-run-time.pl for lena-simple-epc. It simply runs a batch of simulations with a range of parameters and stores the timing results in a CSV file called lteTimes.csv and epcTimes.csv respectively. The range of values each parameter sweeps can be modified editing the corresponding script.

Requirements
The following Perl modules are required to use the provided script, all of them available from CPAN:
  • IO::CaptureOutput
  • Statistics::Descriptive

For installing the modules, simply use the follwing command:

perl -MCPAN -e 'install moduleName'

Plotting results

To plot the results obtained from running the Perl scripts, two gnuplot scripts are provided, in src/lte/test/lte-test-run-plot and src/lte/test/epc-test-run-plot. Most of the plots available in this documentation can be reproduced with those, typing the commands gnuplot < src/lte/test/lte-test-run-plot and gnuplot < src/lte/test/epc-test-run-plot.

Reference software and equipment

All timing tests had been run in a Intel Pentium IV 3.00 GHz machine with 512 Mb of RAM memory running Fedora Core 10 with a 2.6.27.41-170.2.117 kernel, storing the traces directly to the hard disk.

Also, as a reference configuration, the build has been configured static and optimized. The exact waf command issued is:

CXXFLAGS="-O3 -w" ./waf -d optimized configure --enable-static --enable-examples --enable-modules=lte

Results

E-UTRAN

The following results and figures had been obtained with LENA changeset 2c5b0d697717.

Running time

This scenario, evaluates the running time for a fixed simulation time (10s) and Friis propagation mode increasing the number of UEs attached to each eNodeB and the number of planted eNodeBs in the scenario.

_images/runningTime10s.png

Running time

The figure shows the expected behaviour, since it increases linearly respect the number of UEs per eNodeB and quadratically respect the total number of eNodeBs.

Propagation model

The objective of this scenario is to evaluate the impact of the propagation model complexity in the overall run time figures. Therefore, the same scenario is simulated twice: once using the more simple Friis model, once with the more complex Building model. The rest of the parameters (e.g. number of eNodeB and of UE attached per eNodeB) were mantained. The timing results for both models are compared in the following figure.

_images/propagationModel.png

Propagation Model

In this situation, results are also coherent with what is expected. The more complex the model, the higher the running time. Moreover, as the number of computed path losses increases (i.e. more UEs per eNodeB or more eNodeBs) the extra complexity of the propagation model drives the running time figures further apart.

Simulation time

In this scenario, for a fixed set of UEs per eNodeB, different simulation times had been run. As the simulation time increases, running time should also increase linearly, i.e. for a given scenario, simulate four seconds should take twice times what it takes to simulate two seconds. The slope of this line is a function of the complexity of the scenario: the more eNodeB / UEs placed, the higher the slope of the line.

_images/simulationTime.png

Simulation time

Memory usage

Massif tool to profile memory consumption

_images/profiling-memory.png

Memory profile

EPC

The following results and figures had been obtained with LENA changeset e8b3ccdf6673. The rationale behind the two scenarios profiled on this section is the same than for the E-UTRA part.

Running time

Running time evolution is quadratic since we increase at the same time the number of eNodeB and the number of UEs.

_images/epcRunningTime.png

Running time

To estimate the additional complexity of the upper LTE Radio Protocol Stack model and the EPC model, we compare two scenarios using the simplified E-UTRAN version (using only PHY, MAC and the simplified RLC/SM, with no EPC and no ns-3 applications) against the complete E-UTRAN + EPC (with UM RLC, PDCP, end-to-end IP networking and regular ns-3 applications). Both configuration have been tested with the same number of UEs per eNodeB, the same number of eNodeBs, and approximately the same volume of transmitted data (an exact match was not possible due to the different ways in which packets are generated in the two configurations).

_images/epcEutranRunningTime.png

EPC E-UTRAN running time

From the figure, it is evident that the additional complexity of using the upper LTE stack plus the EPC model translates approximately into a doubling of the execution time of the simulations. We believe that, considered all the new features that have been added, this figure is acceptable.

Simulation time

Finally, again the linearity of the running time as the simulation time increases gets validated through a set of experiments, as the following figure shows.

_images/epcSimulationTime.png

Simulation time

References

[TS25814]3GPP TS 25.814 “Physical layer aspect for evolved Universal Terrestrial Radio Access”
[TS29274]3GPP TS 29.274 “Tunnelling Protocol for Control plane (GTPv2-C)”
[TS36101]3GPP TS 36.101 “E-UTRA User Equipment (UE) radio transmission and reception”
[TS36104]3GPP TS 36.104 “E-UTRA Base Station (BS) radio transmission and reception”
[TS36133]3GPP TS 36.133 “E-UTRA Requirements for support of radio resource management”
[TS36211]3GPP TS 36.211 “E-UTRA Physical Channels and Modulation”
[TS36212]3GPP TS 36.212 “E-UTRA Multiplexing and channel coding”
[TS36213]3GPP TS 36.213 “E-UTRA Physical layer procedures”
[TS36214]3GPP TS 36.214 “E-UTRA Physical layer – Measurements”
[TS36300]3GPP TS 36.300 “E-UTRA and E-UTRAN; Overall description; Stage 2”
[TS36304]3GPP TS 36.304 “E-UTRA User Equipment (UE) procedures in idle mode”
[TS36321]3GPP TS 36.321 “E-UTRA Medium Access Control (MAC) protocol specification”
[TS36322]3GPP TS 36.322 “E-UTRA Radio Link Control (RLC) protocol specification”
[TS36323]3GPP TS 36.323 “E-UTRA Packet Data Convergence Protocol (PDCP) specification”
[TS36331]3GPP TS 36.331 “E-UTRA Radio Resource Control (RRC) protocol specification”
[TS36413]3GPP TS 36.413 “E-UTRAN S1 application protocol (S1AP)”
[TS36420]3GPP TS 36.420 “E-UTRAN X2 general aspects and principles”
[TS36423]3GPP TS 36.423 “E-UTRAN X2 application protocol (X2AP)”
[TR36814]3GPP TR 36.814 “E-UTRA Further advancements for E-UTRA physical layer aspects”
[R1-081483]3GPP R1-081483 “Conveying MCS and TB size via PDCCH”
[R4-092042]3GPP R4-092042 “Simulation assumptions and parameters for FDD HeNB RF requirements”
[FFAPI]FemtoForum “LTE MAC Scheduler Interface Specification v1.11”
[ns3tutorial]“The ns-3 Tutorial”
[ns3manual]“The ns-3 Manual”
[Sesia2009]S. Sesia, I. Toufik and M. Baker, “LTE - The UMTS Long Term Evolution - from theory to practice”, Wiley, 2009
[Baldo2009]N. Baldo and M. Miozzo, “Spectrum-aware Channel and PHY layer modeling for ns3”, Proceedings of ICST NSTools 2009, Pisa, Italy
[Piro2010]Giuseppe Piro, Luigi Alfredo Grieco, Gennaro Boggia, and Pietro Camarda, “A Two-level Scheduling Algorithm for QoS Support in the Downlink of LTE Cellular Networks”, Proc. of European Wireless, EW2010, Lucca, Italy, Apr., 2010
[Holtzman2000]J.M. Holtzman, “CDMA forward link waterfilling power control”, in Proc. of IEEE VTC Spring, 2000.
[Piro2011]G. Piro, N. Baldo. M. Miozzo, “An LTE module for the ns-3 network simulator”, in Proc. of Wns3 2011 (in conjunction with SimuTOOLS 2011), March 2011, Barcelona (Spain)
[Seo2004]H. Seo, B. G. Lee. “A proportional-fair power allocation scheme for fair and efficient multiuser OFDM systems”, in Proc. of IEEE GLOBECOM, December 2004. Dallas (USA)
[Ofcom2600MHz]Ofcom, “Consultation on assessment of future mobile competition and proposals for the award of 800 MHz and 2.6 GHz spectrum and related issues”, March 2011
[RealWireless]RealWireless, “Low-power shared access to spectrum for mobile broadband”, Final Report, Ofcom Project MC/073, 18th March 2011
[PaduaPEM]“Ns-developers - LTE error model contribution”
[ViennaLteSim]“The Vienna LTE Simulators”