Deep-reinforcement-learning-based range-adaptive distributed power control for cellular-V2X
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yang, Wooyeol | - |
dc.contributor.author | Jo, Han-Shin | - |
dc.date.accessioned | 2023-11-14T08:16:20Z | - |
dc.date.available | 2023-11-14T08:16:20Z | - |
dc.date.created | 2023-10-31 | - |
dc.date.issued | 2023-08 | - |
dc.identifier.issn | 2405-9595 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/192178 | - |
dc.description.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/). | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | 한국통신학회 | - |
dc.title | Deep-reinforcement-learning-based range-adaptive distributed power control for cellular-V2X | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jo, Han-Shin | - |
dc.identifier.doi | 10.1016/j.icte.2022.07.008 | - |
dc.identifier.scopusid | 2-s2.0-85149819125 | - |
dc.identifier.wosid | 001066731200001 | - |
dc.identifier.bibliographicCitation | ICT Express, v.9, no.4, pp.648 - 655 | - |
dc.relation.isPartOf | ICT Express | - |
dc.citation.title | ICT Express | - |
dc.citation.volume | 9 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 648 | - |
dc.citation.endPage | 655 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002992345 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordAuthor | C-V2X | - |
dc.subject.keywordAuthor | Distributed congestion control | - |
dc.subject.keywordAuthor | Deep reinforcement learning | - |
dc.subject.keywordAuthor | Packet delivery ratio | - |
dc.subject.keywordAuthor | Power control | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S2405959522001059?via%3Dihub | - |
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