There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 2184 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 2180 | - |
dc.citation.title | IEEE WIRELESS COMMUNICATIONS LETTERS | - |
dc.citation.volume | 10 | - |
dc.contributor.author | Yu, Daesung | - |
dc.contributor.author | Lee, Hoon | - |
dc.contributor.author | Park, Seok-Hwan | - |
dc.contributor.author | Hong, Seung-Eun | - |
dc.date.accessioned | 2023-12-21T15:09:00Z | - |
dc.date.available | 2023-12-21T15:09:00Z | - |
dc.date.created | 2023-09-06 | - |
dc.date.issued | 2021-10 | - |
dc.description.abstract | Cooperative beamforming across access points (APs) and fronthaul quantization strategies are essential for cloud radio access network (C-RAN) systems. The nonconvexity of the C-RAN optimization problems, which is stemmed from per-AP power and fronthaul capacity constraints, requires high computational complexity for executing iterative algorithms. To resolve this issue, we investigate a deep learning approach where the optimization module is replaced with a well-trained deep neural network (DNN). An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies. Numerical results validate the advantages of the proposed learning solution. | - |
dc.identifier.bibliographicCitation | IEEE WIRELESS COMMUNICATIONS LETTERS, v.10, no.10, pp.2180 - 2184 | - |
dc.identifier.doi | 10.1109/LWC.2021.3095500 | - |
dc.identifier.issn | 2162-2337 | - |
dc.identifier.scopusid | 2-s2.0-85117167770 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/65448 | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9477494 | - |
dc.identifier.wosid | 000704110300021 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
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 | Quantization (signal) | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Task analysis | - |
dc.subject.keywordAuthor | Downlink | - |
dc.subject.keywordAuthor | Cloud radio access networks | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | beamforming optimization | - |
dc.subject.keywordAuthor | constrained fronthaul | - |
dc.subject.keywordPlus | DOWNLINK | - |
dc.subject.keywordPlus | INTERFERENCE | - |
dc.subject.keywordPlus | SYSTEMS | - |
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.