Multi-Agent Reinforcement Learning Based Resource Allocation for Efficient Message Dissemination in C-V2X Networks

Bingyi Liu, Jingxiang Hao, Enshu Wang, Dongyao Jia, Weizhen Han, Libing Wu, Shengwu Xiong

Abstract: 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.