channel-condition-model-test-suite.cc

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/*
 * Copyright (c) 2019 SIGNET Lab, Department of Information Engineering,
 * University of Padova
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License version 2 as
 * published by the Free Software Foundation;
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 */
 
#include "ns3/abort.h"
#include "ns3/channel-condition-model.h"
#include "ns3/config.h"
#include "ns3/constant-position-mobility-model.h"
#include "ns3/double.h"
#include "ns3/log.h"
#include "ns3/node-container.h"
#include "ns3/simulator.h"
#include "ns3/test.h"
 
using namespace ns3;
 
NS_LOG_COMPONENT_DEFINE("ChannelConditionModelsTest");
 
/**
 * \ingroup propagation-tests
 *
 * Test case for the 3GPP channel condition models. It determines the
 * channel condition multiple times, estimates the LOS probability and
 * compares it with the value given by the formulas in 3GPP TR 38.901,
 * Table Table 7.4.2-1.
 */
class ThreeGppChannelConditionModelTestCase : public TestCase
{
  public:
    /**
     * Constructor
     */
    ThreeGppChannelConditionModelTestCase();
 
    /**
     * Destructor
     */
    ~ThreeGppChannelConditionModelTestCase() override;
 
  private:
    /**
     * Builds the simulation scenario and perform the tests
     */
    void DoRun() override;
 
    /**
     * Evaluates the channel condition between two nodes by calling the method
     * GetChannelCondition on m_condModel. If the channel condition is LOS it
     * increments m_numLos
     * \param a the mobility model of the first node
     * \param b the mobility model of the second node
     */
    void EvaluateChannelCondition(Ptr<MobilityModel> a, Ptr<MobilityModel> b);
 
    /**
     * Struct containing the parameters for each test
     */
    struct TestVector
    {
        Vector m_positionA; //!< the position of the first node
        Vector m_positionB; //!< the position of the second node
        double m_pLos;      //!< LOS probability
        TypeId m_typeId;    //!< the type ID of the channel condition model to be used
    };
 
    TestVectors<TestVector> m_testVectors;          //!< array containing all the test vectors
    Ptr<ThreeGppChannelConditionModel> m_condModel; //!< the channel condition model
    uint64_t m_numLos;                              //!< the number of LOS occurrences
    double m_tolerance;                             //!< tolerance
};
 
ThreeGppChannelConditionModelTestCase::ThreeGppChannelConditionModelTestCase()
    : TestCase("Test case for the child classes of ThreeGppChannelConditionModel"),
      m_testVectors(),
      m_tolerance(2e-3)
{
}
 
ThreeGppChannelConditionModelTestCase::~ThreeGppChannelConditionModelTestCase()
{
}
 
void
ThreeGppChannelConditionModelTestCase::EvaluateChannelCondition(Ptr<MobilityModel> a,
                                                                Ptr<MobilityModel> b)
{
    Ptr<ChannelCondition> cond = m_condModel->GetChannelCondition(a, b);
    if (cond->GetLosCondition() == ChannelCondition::LosConditionValue::LOS)
    {
        m_numLos++;
    }
}
 
void
ThreeGppChannelConditionModelTestCase::DoRun()
{
    // create the test vector
    TestVector testVector;
 
    // tests for the RMa scenario
    testVector.m_positionA = Vector(0, 0, 35.0);
    testVector.m_positionB = Vector(10, 0, 1.5);
    testVector.m_pLos = 1;
    testVector.m_typeId = ThreeGppRmaChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    testVector.m_positionA = Vector(0, 0, 35.0);
    testVector.m_positionB = Vector(100, 0, 1.5);
    testVector.m_pLos = exp(-(100.0 - 10.0) / 1000.0);
    testVector.m_typeId = ThreeGppRmaChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    testVector.m_positionA = Vector(0, 0, 35.0);
    testVector.m_positionB = Vector(1000, 0, 1.5);
    testVector.m_pLos = exp(-(1000.0 - 10.0) / 1000.0);
    testVector.m_typeId = ThreeGppRmaChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    // tests for the UMa scenario
    testVector.m_positionA = Vector(0, 0, 25.0);
    testVector.m_positionB = Vector(18, 0, 1.5);
    testVector.m_pLos = 1;
    testVector.m_typeId = ThreeGppUmaChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    testVector.m_positionA = Vector(0, 0, 25.0);
    testVector.m_positionB = Vector(50, 0, 1.5);
    testVector.m_pLos = (18.0 / 50.0 + exp(-50.0 / 63.0) * (1.0 - 18.0 / 50.0)) * (1.0 + 0);
    testVector.m_typeId = ThreeGppUmaChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    testVector.m_positionA = Vector(0, 0, 25.0);
    testVector.m_positionB = Vector(50, 0, 15);
    testVector.m_pLos =
        (18.0 / 50.0 + exp(-50.0 / 63.0) * (1.0 - 18.0 / 50.0)) *
        (1.0 + pow(2.0 / 10.0, 1.5) * 5.0 / 4.0 * pow(50.0 / 100.0, 3) * exp(-50.0 / 150.0));
    testVector.m_typeId = ThreeGppUmaChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    testVector.m_positionA = Vector(0, 0, 25.0);
    testVector.m_positionB = Vector(100, 0, 1.5);
    testVector.m_pLos = (18.0 / 100.0 + exp(-100.0 / 63.0) * (1.0 - 18.0 / 100.0)) * (1.0 + 0);
    testVector.m_typeId = ThreeGppUmaChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    testVector.m_positionA = Vector(0, 0, 25.0);
    testVector.m_positionB = Vector(100, 0, 15);
    testVector.m_pLos = (18.0 / 100.0 + exp(-100.0 / 63.0) * (1.0 - 18.0 / 100.0)) *
                        (1.0 + pow(2.0 / 10.0, 1.5) * 5.0 / 4.0 * 1.0 * exp(-100.0 / 150.0));
    testVector.m_typeId = ThreeGppUmaChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    // tests for the UMi-Street Canyon scenario
    testVector.m_positionA = Vector(0, 0, 10.0);
    testVector.m_positionB = Vector(18, 0, 1.5);
    testVector.m_pLos = 1;
    testVector.m_typeId = ThreeGppUmiStreetCanyonChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    testVector.m_positionA = Vector(0, 0, 10.0);
    testVector.m_positionB = Vector(50, 0, 1.5);
    testVector.m_pLos = (18.0 / 50.0 + exp(-50.0 / 36.0) * (1.0 - 18.0 / 50.0));
    testVector.m_typeId = ThreeGppUmiStreetCanyonChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    m_testVectors.Add(testVector);
    testVector.m_positionA = Vector(0, 0, 10.0);
    testVector.m_positionB = Vector(100, 0, 15);
    testVector.m_pLos = (18.0 / 100.0 + exp(-100.0 / 36.0) * (1.0 - 18.0 / 100.0));
    testVector.m_typeId = ThreeGppUmiStreetCanyonChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    // tests for the Indoor Mixed Office scenario
    testVector.m_positionA = Vector(0, 0, 2.0);
    testVector.m_positionB = Vector(1.2, 0, 1.5);
    testVector.m_pLos = 1;
    testVector.m_typeId = ThreeGppIndoorMixedOfficeChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    testVector.m_positionA = Vector(0, 0, 2.0);
    testVector.m_positionB = Vector(5, 0, 1.5);
    testVector.m_pLos = exp(-(5.0 - 1.2) / 4.7);
    testVector.m_typeId = ThreeGppIndoorMixedOfficeChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    testVector.m_positionA = Vector(0, 0, 2.0);
    testVector.m_positionB = Vector(10, 0, 1.5);
    testVector.m_pLos = exp(-(10.0 - 6.5) / 32.6) * 0.32;
    testVector.m_typeId = ThreeGppIndoorMixedOfficeChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    // tests for the Indoor Open Office scenario
    testVector.m_positionA = Vector(0, 0, 3.0);
    testVector.m_positionB = Vector(5, 0, 1.5);
    testVector.m_pLos = 1;
    testVector.m_typeId = ThreeGppIndoorOpenOfficeChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    testVector.m_positionA = Vector(0, 0, 3.0);
    testVector.m_positionB = Vector(30, 0, 1.5);
    testVector.m_pLos = exp(-(30.0 - 5.0) / 70.8);
    testVector.m_typeId = ThreeGppIndoorOpenOfficeChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    testVector.m_positionA = Vector(0, 0, 3.0);
    testVector.m_positionB = Vector(100, 0, 1.5);
    testVector.m_pLos = exp(-(100.0 - 49.0) / 211.7) * 0.54;
    testVector.m_typeId = ThreeGppIndoorOpenOfficeChannelConditionModel::GetTypeId();
    m_testVectors.Add(testVector);
 
    // create the factory for the channel condition models
    ObjectFactory condModelFactory;
 
    // create the two nodes
    NodeContainer nodes;
    nodes.Create(2);
 
    // create the mobility models
    Ptr<MobilityModel> a = CreateObject<ConstantPositionMobilityModel>();
    Ptr<MobilityModel> b = CreateObject<ConstantPositionMobilityModel>();
 
    // aggregate the nodes and the mobility models
    nodes.Get(0)->AggregateObject(a);
    nodes.Get(1)->AggregateObject(b);
 
    // Get the channel condition multiple times and compute the LOS probability
    uint32_t numberOfReps = 500000;
    for (uint32_t i = 0; i < m_testVectors.GetN(); ++i)
    {
        testVector = m_testVectors.Get(i);
 
        // set the distance between the two nodes
        a->SetPosition(testVector.m_positionA);
        b->SetPosition(testVector.m_positionB);
 
        // create the channel condition model
        condModelFactory.SetTypeId(testVector.m_typeId);
        m_condModel = condModelFactory.Create<ThreeGppChannelConditionModel>();
        m_condModel->SetAttribute("UpdatePeriod", TimeValue(MilliSeconds(9)));
 
        m_numLos = 0;
        for (uint32_t j = 0; j < numberOfReps; j++)
        {
            Simulator::Schedule(MilliSeconds(10 * j),
                                &ThreeGppChannelConditionModelTestCase::EvaluateChannelCondition,
                                this,
                                a,
                                b);
        }
 
        Simulator::Run();
        Simulator::Destroy();
 
        double resultPlos = double(m_numLos) / double(numberOfReps);
        NS_LOG_DEBUG(testVector.m_typeId << "  a pos " << testVector.m_positionA << " b pos "
                                         << testVector.m_positionB << " numLos " << m_numLos
                                         << " numberOfReps " << numberOfReps << " resultPlos "
                                         << resultPlos << " ref " << testVector.m_pLos);
        NS_TEST_EXPECT_MSG_EQ_TOL(resultPlos,
                                  testVector.m_pLos,
                                  m_tolerance,
                                  "Got unexpected LOS probability");
    }
}
 
/**
 * \ingroup propagation-tests
 *
 * Test suite for the channel condition models
 */
class ChannelConditionModelsTestSuite : public TestSuite
{
  public:
    ChannelConditionModelsTestSuite();
};
 
ChannelConditionModelsTestSuite::ChannelConditionModelsTestSuite()
    : TestSuite("propagation-channel-condition-model", Type::UNIT)
{
    AddTestCase(new ThreeGppChannelConditionModelTestCase, TestCase::Duration::QUICK);
}
 
/// Static variable for test initialization
static ChannelConditionModelsTestSuite g_channelConditionModelsTestSuite;