There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 1898 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 1894 | - |
dc.citation.title | IEEE WIRELESS COMMUNICATIONS LETTERS | - |
dc.citation.volume | 9 | - |
dc.contributor.author | Kim, Junbeom | - |
dc.contributor.author | Lee, Hoon | - |
dc.contributor.author | Hong, Seung-Eun | - |
dc.contributor.author | Park, Seok-Hwan | - |
dc.date.accessioned | 2023-12-21T16:40:49Z | - |
dc.date.available | 2023-12-21T16:40:49Z | - |
dc.date.created | 2023-09-06 | - |
dc.date.issued | 2020-11 | - |
dc.description.abstract | This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes. | - |
dc.identifier.bibliographicCitation | IEEE WIRELESS COMMUNICATIONS LETTERS, v.9, no.11, pp.1894 - 1898 | - |
dc.identifier.doi | 10.1109/LWC.2020.3007198 | - |
dc.identifier.issn | 2162-2337 | - |
dc.identifier.scopusid | 2-s2.0-85096090983 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/65454 | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9134393 | - |
dc.identifier.wosid | 000589198200021 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Deep Learning Methods for Universal MISO Beamforming | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications | - |
dc.relation.journalResearchArea | Computer Science; Engineering; Telecommunications | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Array signal processing | - |
dc.subject.keywordAuthor | Optimization | - |
dc.subject.keywordAuthor | Downlink | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | MISO communication | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | Multi-user MISO downlink | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | beamforming | - |
dc.subject.keywordAuthor | interference management | - |
dc.subject.keywordAuthor | unsupervised learning | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.