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DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 9658 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 9653 | - |
dc.citation.title | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | - |
dc.citation.volume | 72 | - |
dc.contributor.author | Yu, Daesung | - |
dc.contributor.author | Lee, Hoon | - |
dc.contributor.author | Hong, Seung-Eun | - |
dc.contributor.author | Park, Seok-Hwan | - |
dc.date.accessioned | 2023-12-21T11:50:23Z | - |
dc.date.available | 2023-12-21T11:50:23Z | - |
dc.date.created | 2023-09-05 | - |
dc.date.issued | 2023-07 | - |
dc.description.abstract | This paper studies learning-based decentralized power control methods for cell-free massive multiple-input multiple-output (MIMO) systems where a central processor (CP) controls access points (APs) through fronthaul coordination. To determine the transmission policy of distributed APs, it is essential to develop a network-wide collaborative optimization mechanism. To address this challenge, we design a cooperative learning (CL) framework which manages computation and coordination strategies of the CP and APs using dedicated deep neural network (DNN) modules. To build a versatile learning structure, the proposed CL is carefully designed such that its forward pass calculations are independent of the number of APs. To this end, we adopt a parameter reuse concept which installs an identical DNN module at all APs. Consequently, the proposed CL trained at a particular configuration can be readily applied to arbitrary AP populations. Numerical results validate the advantages of the proposed CL over conventional non-cooperative approaches. | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.72, no.7, pp.9653 - 9658 | - |
dc.identifier.doi | 10.1109/TVT.2023.3251415 | - |
dc.identifier.issn | 0018-9545 | - |
dc.identifier.scopusid | 2-s2.0-85149423057 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/65433 | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/10058112 | - |
dc.identifier.wosid | 001040905800110 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Learning Decentralized Power Control in Cell-Free Massive MIMO 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 | Cell-free MIMO | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | power control | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | ALLOCATION | - |
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