Vision-Aided Beam Allocation for Indoor mmWave Communications
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sarker, Md. Abdul Latif | - |
dc.contributor.author | Orikumhi, Igbafe | - |
dc.contributor.author | Kang, Jeongwan | - |
dc.contributor.author | Jwa, Hye-Kyung | - |
dc.contributor.author | Na, Jee-Hyeon | - |
dc.contributor.author | Kim, Sunwoo | - |
dc.date.accessioned | 2022-07-06T10:54:52Z | - |
dc.date.available | 2022-07-06T10:54:52Z | - |
dc.date.created | 2022-01-26 | - |
dc.date.issued | 2021-12 | - |
dc.identifier.issn | 2162-1233 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140084 | - |
dc.description.abstract | This paper presents a vision-aided beam allocation scheme to help conquer the non-trivial issue such as blockage or link failure scenarios of the millimeter wave (mmWave) indoor wireless communication systems. Particularly, a traditional beam allocation scheme degrades the beam training performance due to a non-convex optimization problem, which contain a combinatorial number of local optima and make them extremely challenging for conventional solvers. Hence, we propose a vision-aided beam allocation scheme to overcome the beam optimization issue and enhance the beam training performance in this paper. We employ a camera at the mmWave access point and leverage their scene information to spontaneously sort out the best allocated beam. We also exploit a machine learning tool to predict the allocated mmWave beam from the camera RGB scene. The simulation results show the performance of the proposed vision-aided solutions in terms of beam training and testing performance. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Vision-Aided Beam Allocation for Indoor mmWave Communications | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Orikumhi, Igbafe | - |
dc.contributor.affiliatedAuthor | Kim, Sunwoo | - |
dc.identifier.doi | 10.1109/ICTC52510.2021.9621174 | - |
dc.identifier.scopusid | 2-s2.0-85122941159 | - |
dc.identifier.wosid | 000790235800342 | - |
dc.identifier.bibliographicCitation | International Conference on ICT Convergence, v.2021, no.October, pp.1403 - 1408 | - |
dc.relation.isPartOf | International Conference on ICT Convergence | - |
dc.citation.title | International Conference on ICT Convergence | - |
dc.citation.volume | 2021 | - |
dc.citation.number | October | - |
dc.citation.startPage | 1403 | - |
dc.citation.endPage | 1408 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | Cameras | - |
dc.subject.keywordPlus | Computer vision | - |
dc.subject.keywordPlus | Convex optimization | - |
dc.subject.keywordPlus | Machine learning | - |
dc.subject.keywordPlus | Accuracy and loss performance | - |
dc.subject.keywordPlus | Beam allocation | - |
dc.subject.keywordPlus | Beam allocation scheme | - |
dc.subject.keywordPlus | Indoor communications | - |
dc.subject.keywordPlus | Link failures | - |
dc.subject.keywordPlus | Loss performance | - |
dc.subject.keywordPlus | Millimeterwave communications | - |
dc.subject.keywordPlus | Non-trivial | - |
dc.subject.keywordPlus | Vision-aided millimeter wave indoor communication | - |
dc.subject.keywordPlus | Millimeter waves | - |
dc.subject.keywordAuthor | accuracy and loss performance | - |
dc.subject.keywordAuthor | beam allocation scheme | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | Vision-aided mmWave indoor communications | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9621174 | - |
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