Machine Learning-based Vision-aided Beam Selection for mmWave Multi-User MISO System
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahn, Hyemin | - |
dc.contributor.author | Orikumhi, Igbafe | - |
dc.contributor.author | Kang, Jeongwan | - |
dc.contributor.author | Park, Hyunwoo | - |
dc.contributor.author | Jwa, Hyekyung | - |
dc.contributor.author | Na, Jeehyeon | - |
dc.contributor.author | Kim, Sunwoo | - |
dc.date.accessioned | 2022-07-19T05:00:41Z | - |
dc.date.available | 2022-07-19T05:00:41Z | - |
dc.date.created | 2022-05-04 | - |
dc.date.issued | 2022-06 | - |
dc.identifier.issn | 2162-2337 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/170129 | - |
dc.description.abstract | In this paper, we propose a machine learning-based vision-aided beam selection (ML-VBS) for millimeter-wave indoor multi-user communications. The proposed scheme is aimed at addressing the beam selection overhead with narrow beams in a multi-user scenario. The proposed scheme relies on a base station (BS) equipped with a single camera to observe the scene and estimates the angles to the multiple users. Given the estimated angle information and the limited number of radio frequency chains at the BS, two serial deep neural network structures are employed for joint user and beam selection subject to a minimum rate constraint. The numerical evaluation shows that the proposed ML-VBS scheme achieves a good performance in terms of the multi-user angle estimation, achievable sum rate and low computational complexity compared to conventional beam selection techniques. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Machine Learning-based Vision-aided Beam Selection for mmWave Multi-User MISO System | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Orikumhi, Igbafe | - |
dc.contributor.affiliatedAuthor | Kim, Sunwoo | - |
dc.identifier.doi | 10.1109/LWC.2022.3163780 | - |
dc.identifier.scopusid | 2-s2.0-85127522882 | - |
dc.identifier.wosid | 000808068800035 | - |
dc.identifier.bibliographicCitation | IEEE Wireless Communications Letters, v.11, no.6, pp.1263 - 1267 | - |
dc.relation.isPartOf | IEEE Wireless Communications Letters | - |
dc.citation.title | IEEE Wireless Communications Letters | - |
dc.citation.volume | 11 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 1263 | - |
dc.citation.endPage | 1267 | - |
dc.type.rims | ART | - |
dc.type.docType | Article in Press | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | MILLIMETER-WAVE | - |
dc.subject.keywordPlus | CHANNEL | - |
dc.subject.keywordAuthor | beam selection | - |
dc.subject.keywordAuthor | hybrid beamforming | - |
dc.subject.keywordAuthor | indoor communications | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | Vision-aided | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9745525 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
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.