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DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 14060 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 14055 | - |
dc.citation.title | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | - |
dc.citation.volume | 69 | - |
dc.contributor.author | Hwang, Sangwon | - |
dc.contributor.author | Kim, Hanjin | - |
dc.contributor.author | Lee, Hoon | - |
dc.contributor.author | Lee, Inkyu | - |
dc.date.accessioned | 2023-12-21T16:40:51Z | - |
dc.date.available | 2023-12-21T16:40:51Z | - |
dc.date.created | 2023-09-06 | - |
dc.date.issued | 2020-11 | - |
dc.description.abstract | This paper studies multi-agent deep reinforcement learning (MADRL) based resource allocation methods for multi-cell wireless powered communication networks (WPCNs) where multiple hybrid access points (H-APs) wirelessly charge energy-limited users to collect data from them. We design a distributed reinforcement learning strategy where H-APs individually determine time and power allocation variables. Unlike traditional centralized optimization algorithms which require global information collected at a central unit, the proposed MADRL technique models an H-AP as an agent producing its action based only on its own locally observable states. Numerical results verify that the proposed approach can achieve comparable performance of the centralized algorithms. | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.69, no.11, pp.14055 - 14060 | - |
dc.identifier.doi | 10.1109/TVT.2020.3029609 | - |
dc.identifier.issn | 0018-9545 | - |
dc.identifier.scopusid | 2-s2.0-85096222511 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/65455 | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9217951 | - |
dc.identifier.wosid | 000589638700143 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Multi-Agent Deep Reinforcement Learning for Distributed Resource Management in Wirelessly Powered Communication Networks | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic; Telecommunications; Transportation Science & Technology | - |
dc.relation.journalResearchArea | Engineering; Telecommunications; Transportation | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Resource management | - |
dc.subject.keywordAuthor | Interference | - |
dc.subject.keywordAuthor | Optimization | - |
dc.subject.keywordAuthor | Uplink | - |
dc.subject.keywordAuthor | Wireless communication | - |
dc.subject.keywordAuthor | Downlink | - |
dc.subject.keywordAuthor | Wireless sensor networks | - |
dc.subject.keywordAuthor | Wireless powered communication networks | - |
dc.subject.keywordAuthor | multi-agent deep reinforcement learning | - |
dc.subject.keywordAuthor | actor-critic method | - |
dc.subject.keywordPlus | ALLOCATION | - |
dc.subject.keywordPlus | MAXIMIZATION | - |
dc.subject.keywordPlus | INFORMATION | - |
dc.subject.keywordPlus | NOMA | - |
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