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

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2024 IEEE/ACM 32nd International Symposium on Quality of Service, IWQoS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350350128
DOIs
Publication statusPublished - 2024
Event32nd IEEE/ACM International Symposium on Quality of Service, IWQoS 2024 - Guangzhou, China
Duration: 19 Jun 202421 Jun 2024

Publication series

NameIEEE International Workshop on Quality of Service, IWQoS
ISSN (Print)1548-615X

Conference

Conference32nd IEEE/ACM International Symposium on Quality of Service, IWQoS 2024
Country/TerritoryChina
CityGuangzhou
Period19/06/2421/06/24

Keywords

  • C-V2X
  • Multi-agent Reinforcement Learning
  • Resource Allocation

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