Difference between revisions of "GSOC2024RLUsability5G"
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* '''Student:''' Hyerin Kim | * '''Student:''' Hyerin Kim | ||
* '''Mentors:''' Amir Ashtari, Katerina Koutlia, Bijana Bojovic, Gabriel Ferreira | * '''Mentors:''' Amir Ashtari, Katerina Koutlia, Bijana Bojovic, Gabriel Ferreira | ||
| − | * '''Google page:''' | + | * '''Google page:''' https://summerofcode.withgoogle.com/programs/2024/projects/vPuZgTe1 |
| − | * '''Project Goals:''' | + | * '''Project Goals:''' In this project, I will improve the interface between OpenGymEnv in ns3 and ns3env in the Python ns3 gym module for Multi-Agent Reinforcement Learning (MARL). Currently, ns3gym utilizes the REQ-REP pattern in ZeroMQ as the interface. By incorporating techniques such as client identity and parallel processing of workers in ZMQ, I aim to develop a MARL interface in ns3gym. Additionally, I will enhance the usability of 5G by implementing MARL approaches in 5G examples, with a specific focus on 5G LENA. |
| − | * '''Repository:''' | + | * '''Repository:''' https://github.com/mye280c37/GSoC-2024 |
| − | * '''About Me:''' | + | * '''About Me:''' I am currently pursuing a Master's degree in Computer Science and Engineering at Seoul National University, Korea. My research at the Mobile Computing & Communications Laboratory focuses on resource allocation methods in NR V2X Sidelink. As an undergraduate, I conducted research on improving spatial reuse in dense Wi-Fi environments and implemented a Reinforcement Learning (RL)-based modified OBSS/PD algorithm using ns3 gym. I believe that participating in GSoC 2024 presents an excellent opportunity for me to contribute to enhancing the usability of 5G and RL experiences on ns-3, while also deepening my understanding of 5G technology, mechanisms, and system architecture. |
| + | |||
= Milestones = | = Milestones = | ||
| + | |||
| + | === Phase1. Design example (3 weeks) === | ||
| + | |||
| + | * Familiar with 5g-lena ''(2 weeks)'' | ||
| + | * Design Scenario (e.g., UEs deployment, UEs speed, cell configuration, …) ''(1 week)'' | ||
| + | ::- Define Assumption (e.g., delay, TDMA/OFDMA, …) | ||
| + | |||
| + | === Phase2. Design RL based Scheduler (6 +1 weeks) === | ||
| + | |||
| + | * Design scheduler ''(2 weeks)'' | ||
| + | ::- input/output | ||
| + | ::- goal of optimization | ||
| + | * Design RL process ''(1 week)'' | ||
| + | ::- Define suitable RL techniques considering optimization objective of the scheduler and computational complexity | ||
| + | * Implementation of RL based scheduler in 5g lena ''(3 +1 weeks)'' | ||
| + | ::- Create the test | ||
| + | ::- Create documentation | ||
| + | ::- Create MR to 5g lena '''''(Milestone 1)''''' | ||
| + | |||
| + | === Phase3. RL Integration (4 weeks) === | ||
| + | |||
| + | * Design RL framework ''(1 week)'' | ||
| + | ::- Define RL technique | ||
| + | * Develop gym scripts ''(3 weeks)'' | ||
| + | ::- Develop gym python scripts | ||
| + | ::- Develop ns3 gym interface in RL 5g lena example | ||
| + | ::- Validate RL process of the example | ||
| + | ::- Create MR to 5g lena '''''(Milestone 2)''''' | ||
| + | |||
| + | === Phase4. Evaluation (3 +1 weeks)=== | ||
| + | |||
| + | * Evaluate the result of example compared with other schedulers | ||
| + | ::- Write simulation campaign scripts | ||
| + | ::- Execute scripts | ||
| + | ::- Plotting python scripts | ||
| + | * Address review comment of the MR 1 and 2 | ||
| + | * Update MR 1 and 2 with necessary modification | ||
| + | * Create brief description of the work and the results for 5g lena blog '''''(Milestone 3)''''' | ||
Revision as of 12:01, 15 May 2024
Main Page - Current Development - Developer FAQ - Tools - Related Projects - Project Ideas - Summer Projects
Installation - Troubleshooting - User FAQ - HOWTOs - Samples - Models - Education - Contributed Code - Papers
Back to GSoC 2024 projects
Contents
Project Overview
- Project Name: Enhancement of RL Approach Accessibility in NR
- Student: Hyerin Kim
- Mentors: Amir Ashtari, Katerina Koutlia, Bijana Bojovic, Gabriel Ferreira
- Google page: https://summerofcode.withgoogle.com/programs/2024/projects/vPuZgTe1
- Project Goals: In this project, I will improve the interface between OpenGymEnv in ns3 and ns3env in the Python ns3 gym module for Multi-Agent Reinforcement Learning (MARL). Currently, ns3gym utilizes the REQ-REP pattern in ZeroMQ as the interface. By incorporating techniques such as client identity and parallel processing of workers in ZMQ, I aim to develop a MARL interface in ns3gym. Additionally, I will enhance the usability of 5G by implementing MARL approaches in 5G examples, with a specific focus on 5G LENA.
- Repository: https://github.com/mye280c37/GSoC-2024
- About Me: I am currently pursuing a Master's degree in Computer Science and Engineering at Seoul National University, Korea. My research at the Mobile Computing & Communications Laboratory focuses on resource allocation methods in NR V2X Sidelink. As an undergraduate, I conducted research on improving spatial reuse in dense Wi-Fi environments and implemented a Reinforcement Learning (RL)-based modified OBSS/PD algorithm using ns3 gym. I believe that participating in GSoC 2024 presents an excellent opportunity for me to contribute to enhancing the usability of 5G and RL experiences on ns-3, while also deepening my understanding of 5G technology, mechanisms, and system architecture.
Milestones
Phase1. Design example (3 weeks)
- Familiar with 5g-lena (2 weeks)
- Design Scenario (e.g., UEs deployment, UEs speed, cell configuration, …) (1 week)
- - Define Assumption (e.g., delay, TDMA/OFDMA, …)
Phase2. Design RL based Scheduler (6 +1 weeks)
- Design scheduler (2 weeks)
- - input/output
- - goal of optimization
- Design RL process (1 week)
- - Define suitable RL techniques considering optimization objective of the scheduler and computational complexity
- Implementation of RL based scheduler in 5g lena (3 +1 weeks)
- - Create the test
- - Create documentation
- - Create MR to 5g lena (Milestone 1)
Phase3. RL Integration (4 weeks)
- Design RL framework (1 week)
- - Define RL technique
- Develop gym scripts (3 weeks)
- - Develop gym python scripts
- - Develop ns3 gym interface in RL 5g lena example
- - Validate RL process of the example
- - Create MR to 5g lena (Milestone 2)
Phase4. Evaluation (3 +1 weeks)
- Evaluate the result of example compared with other schedulers
- - Write simulation campaign scripts
- - Execute scripts
- - Plotting python scripts
- Address review comment of the MR 1 and 2
- Update MR 1 and 2 with necessary modification
- Create brief description of the work and the results for 5g lena blog (Milestone 3)
