GSOC2024RLUsability5G: Difference between revisions
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* '''Mentors:''' Amir Ashtari, Katerina Koutlia, Bijana Bojovic, Gabriel Ferreira | * '''Mentors:''' Amir Ashtari, Katerina Koutlia, Bijana Bojovic, Gabriel Ferreira | ||
* '''Google page:''' https://summerofcode.withgoogle.com/programs/2024/projects/vPuZgTe1 | * '''Google page:''' https://summerofcode.withgoogle.com/programs/2024/projects/vPuZgTe1 | ||
* '''Project Goals:''' In this project, I will | * '''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 | * '''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. | * '''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 = | ||
Revision as of 07:04, 4 May 2024
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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.