TY - GEN
T1 - Multi-Agent Reinforcement Learning Based Resource Allocation for Efficient Message Dissemination in C-V2X Networks
AU - Liu, Bingyi
AU - Hao, Jingxiang
AU - Wang, Enshu
AU - Jia, Dongyao
AU - Han, Weizhen
AU - Wu, Libing
AU - Xiong, Shengwu
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In order to support diverse applications in intelligent transportation, intelligent connected vehicles (ICVs) need to send multiple types of messages, such as periodic messages and event-driven messages with different frame specifications. However, existing researches often concentrate on the transmission of single-message types, overlooking hybrid communication scenarios where multiple types of messages coexist, posing challenges in meeting the diverse transmission needs of different message types. To optimize the Quality of Service (QoS) in such scenarios, we take the perspective of ICVs and formulate their decision making as a multi-agent reinforcement learning problem. More specifically, we propose a cooperative individual rewards assisted multi-agent reinforcement learning (CIRA) framework. The transformer structure in CIRA is used to avoid mutual interference during the transmission of different vehicles. Besides, the introduction of individual rewards and the dual-layer architecture of CIRA contribute to providing ICVs with more forward-looking message dissemination scheme. Finally, we set up a simulator to create dynamic traffic scenarios reflecting different real-world conditions. We conduct extensive experiments to evaluate the proposed CIRA framework's performance. The results show that CIRA can significantly improve the packet reception rates and ensure low communication delays in various scenarios.
AB - In order to support diverse applications in intelligent transportation, intelligent connected vehicles (ICVs) need to send multiple types of messages, such as periodic messages and event-driven messages with different frame specifications. However, existing researches often concentrate on the transmission of single-message types, overlooking hybrid communication scenarios where multiple types of messages coexist, posing challenges in meeting the diverse transmission needs of different message types. To optimize the Quality of Service (QoS) in such scenarios, we take the perspective of ICVs and formulate their decision making as a multi-agent reinforcement learning problem. More specifically, we propose a cooperative individual rewards assisted multi-agent reinforcement learning (CIRA) framework. The transformer structure in CIRA is used to avoid mutual interference during the transmission of different vehicles. Besides, the introduction of individual rewards and the dual-layer architecture of CIRA contribute to providing ICVs with more forward-looking message dissemination scheme. Finally, we set up a simulator to create dynamic traffic scenarios reflecting different real-world conditions. We conduct extensive experiments to evaluate the proposed CIRA framework's performance. The results show that CIRA can significantly improve the packet reception rates and ensure low communication delays in various scenarios.
KW - C-V2X
KW - Multi-agent Reinforcement Learning
KW - Resource Allocation
UR - http://www.scopus.com/inward/record.url?scp=85206362573&partnerID=8YFLogxK
U2 - 10.1109/IWQoS61813.2024.10682924
DO - 10.1109/IWQoS61813.2024.10682924
M3 - Conference Proceeding
AN - SCOPUS:85206362573
T3 - IEEE International Workshop on Quality of Service, IWQoS
BT - 2024 IEEE/ACM 32nd International Symposium on Quality of Service, IWQoS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE/ACM International Symposium on Quality of Service, IWQoS 2024
Y2 - 19 June 2024 through 21 June 2024
ER -