Deep-reinforcement-learning-based range-adaptive distributed power control for cellular-V2Xopen access
- Authors
- Yang, Wooyeol; Jo, Han-Shin
- Issue Date
- Aug-2023
- Publisher
- 한국통신학회
- Keywords
- C-V2X; Distributed congestion control; Deep reinforcement learning; Packet delivery ratio; Power control
- Citation
- ICT Express, v.9, no.4, pp.648 - 655
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- ICT Express
- Volume
- 9
- Number
- 4
- Start Page
- 648
- End Page
- 655
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/192178
- DOI
- 10.1016/j.icte.2022.07.008
- ISSN
- 2405-9595
- Abstract
- A distributed congestion control must be adaptable to varying target communication ranges as cellular V2X (C-V2X) is evolving to support flexible coverage suitable for various service scenarios. This study proposes range-adaptive distributed power control (Ra-DPC) based on deep reinforcement learning (DRL) with the Monte Carlo policy gradient algorithm. A key finding is that the agents learn Ra-DPC more effectively when the cumulative interference power of the subchannels is adopted as the state of the DRL model, rather than the channel busy ratio. The proposed Ra-DPC algorithm performs better in energy efficiency and packet delivery ratio than the existing technologies. & COPY; 2022 The Authors. Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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