Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Hyebin | - |
dc.contributor.author | Lim, Yujin | - |
dc.date.available | 2021-02-22T05:35:29Z | - |
dc.date.issued | 2020-04 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-3210 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/2474 | - |
dc.description.abstract | Increased data traffic resulting from the increase in the deployment of connected vehicles has become relevant in vehicular social networks (VSNs). To provide efficient communication between connected vehicles, researchers have studied device-to-device (D2D) communication. D2D communication not only reduces the energy consumption and loads of the system but also increases the system capacity by reusing cellular resources. However, D2D communication is highly affected by interference and therefore requires interference-management techniques, such as mode selection and power control. To make an optimal mode selection and power control, it is necessary to apply reinforcement learning that considers a variety of factors. In this paper, we propose a reinforcement-learning technique for energy optimization with fifth-generation communication in VSNs. To achieve energy optimization, we use centralized Q-learning in the system and distributed Q-learning in the vehicles. The proposed algorithm learns to maximize the energy efficiency of the system by adjusting the minimum signal-to-interference plus noise ratio to guarantee the outage probability. Simulations were performed to compare the performance of the proposed algorithm with that of the existing mode-selection and power-control algorithms. The proposed algorithm performed the best in terms of system energy efficiency and achievable data rate. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/s20082361 | - |
dc.identifier.scopusid | 2-s2.0-85083969977 | - |
dc.identifier.wosid | 000533346400203 | - |
dc.identifier.bibliographicCitation | SENSORS, v.20, no.8 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 20 | - |
dc.citation.number | 8 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | MODE SELECTION | - |
dc.subject.keywordPlus | D2D COMMUNICATIONS | - |
dc.subject.keywordAuthor | 5G | - |
dc.subject.keywordAuthor | D2D communication | - |
dc.subject.keywordAuthor | vehicle-to-vehicle communication | - |
dc.subject.keywordAuthor | mode selection | - |
dc.subject.keywordAuthor | power control | - |
dc.subject.keywordAuthor | vehicular social network | - |
dc.identifier.url | https://www.mdpi.com/1424-8220/20/8/2361 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Sookmyung Women's University. Cheongpa-ro 47-gil 100 (Cheongpa-dong 2ga), Yongsan-gu, Seoul, 04310, Korea02-710-9127
Copyright©Sookmyung Women's University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.