Deep reinforcement learning-based dual-mode congestion control for cellular V2X environments

Yeomyung Yoon, Hojeong Lee, Hyogon Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The Society of Automotive Engineers (SAE) J2945/1 standard for Dedicated Short Range Communication (DSRC) environmentutilizes transmit (Tx) power control and rate control elements for the periodic BSM transmissions, which are intended to work in a complementary manner. An equivalent standard for the cellular vehicle-to-everything (C-V2X) communication environment is J3161/1, but it eliminates Tx power control and uses only rate control. However, the consequence is the degraded update delay of neighbouring vehicles’ kinematics, potentially undermineing driving safety. In this Letter, the authors propose to retain the dual-mode control in the C-V2X environment and find a policy through reinforcement learning (RL) to adjust the rate control function to maintain synergy. Moreover, the authors can extract the RL-created policy from the neural network so that it can be explicitly specified in the standard, and downloaded and used more conveniently by vehicles. Finally, the RL-generated policy achieves a better packet delivery frequency than J2945/1 or J3161/1.

Original languageEnglish
Article numbere12984
JournalElectronics Letters
Volume59
Issue number20
DOIs
Publication statusPublished - 2023 Oct

Bibliographical note

Publisher Copyright:
© 2023 The Authors. Electronics Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Keywords

  • artificial intelligence
  • cellular radio
  • telecommunication congestion control
  • vehicular ad hoc networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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