Spectrum Management with Congestion Avoidance for V2X Based on Multi-Agent Reinforcement Learning

Ibrahim Althamary, Jun Yong Lin, Chih Wei Huang

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

Abstract

Multi-agent reinforcement learning (MARL) for vehicular communication management is an emerging topic attracting considerable research attention. In this paper, to enhance the system throughput and spectrum efficiency, the vehicular agents can select different transmission modes, power, and sub-channels to maximize the overall system throughput in a decentralized manner. We propose a novel MARL resource allocation algorithm capable of congestion avoidance for vehicular networks with a multi-agent extension of advantage actorcritic (A2C). The cooperative action and congestion avoidance are achieved by global rewards and a unique dump channel respectively. Moreover, comparison with landmark schemes is conducted on the realistic setup. The result shows that the agent achieves favorable performance with the proposed scheme to the environment.

Original languageEnglish
Title of host publication2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173078
DOIs
StatePublished - Dec 2020
Event2020 IEEE Globecom Workshops, GC Wkshps 2020 - Virtual, Taipei, Taiwan
Duration: 7 Dec 202011 Dec 2020

Publication series

Name2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

Conference

Conference2020 IEEE Globecom Workshops, GC Wkshps 2020
Country/TerritoryTaiwan
CityVirtual, Taipei
Period7/12/2011/12/20

Keywords

  • 5G
  • Multi-agent reinforcement learning (MARL)
  • resource allocation
  • spectrum management
  • vehicle-to-everything (V2X)

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