Deep Neural Network for Beam and Blockage Prediction in 3GPP-Based Indoor Hotspot Environments
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Moon, Sangmi | - |
dc.contributor.author | Kim, Hyeonsung | - |
dc.contributor.author | You, Young-Hwan | - |
dc.contributor.author | Kim, Cheol Hong | - |
dc.contributor.author | Hwang, Intae | - |
dc.date.accessioned | 2022-10-14T02:40:07Z | - |
dc.date.available | 2022-10-14T02:40:07Z | - |
dc.date.created | 2022-03-11 | - |
dc.date.issued | 2022-06 | - |
dc.identifier.issn | 0929-6212 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/42507 | - |
dc.description.abstract | The application of millimeter-wave (mm-wave) frequencies in communication has the potential to address the ever-growing data traffic requirements of next-generation wireless communication devices. However, owing to their high directivity and high penetration loss, directional mm-wave beams are vulnerable to blockages caused by users' bodies and ambient obstacles. Further development of indoor mm-wave communication is essential, as the majority of data traffic is generated in indoor environments. In previous studies, the mm-wave blockage problem was primarily considered in outdoor scenarios, whereas in the present study, online learning-based beam and blockage prediction in an indoor hotspot (InH) scenario was investigated. During an offline training phase, we constructed a fingerprinting database consisting of user locations along with their respective data traffic demands and corresponding blockage statuses with optimal beam indices. Following its creation, the fingerprinting database was used to train the weights and bias of a properly designed deep neural network (DNN). During a subsequent online learning phase, the trained DNN was fed user locations and corresponding data traffic demands at the served user equipment to output optimal beam indices and blockage statuses. System-level simulations were conducted to assess the accuracy of blockage prediction based on 3GPP's new radio channel and blockage models in InH environments. Simulation results revealed that the proposed scheme was capable of predicting mm-wave blockages with an accuracy of > 90%. These results confirmed the viability of the proposed DNN model for predicting optimal mm-wave beams and spectral efficiencies. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.relation.isPartOf | WIRELESS PERSONAL COMMUNICATIONS | - |
dc.title | Deep Neural Network for Beam and Blockage Prediction in 3GPP-Based Indoor Hotspot Environments | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s11277-022-09513-4 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | WIRELESS PERSONAL COMMUNICATIONS, v.124, no.4, pp.3287 - 3306 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000743857900006 | - |
dc.identifier.scopusid | 2-s2.0-85123087874 | - |
dc.citation.endPage | 3306 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 3287 | - |
dc.citation.title | WIRELESS PERSONAL COMMUNICATIONS | - |
dc.citation.volume | 124 | - |
dc.contributor.affiliatedAuthor | Kim, Cheol Hong | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | Blockage | - |
dc.subject.keywordAuthor | DNN | - |
dc.subject.keywordAuthor | Indoor | - |
dc.subject.keywordAuthor | Mm-wave | - |
dc.subject.keywordAuthor | Online learning | - |
dc.subject.keywordPlus | MILLIMETER-WAVE COMMUNICATIONS | - |
dc.subject.keywordPlus | CHANNEL ESTIMATION | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Soongsil University Library 369 Sangdo-Ro, Dongjak-Gu, Seoul, Korea (06978)02-820-0733
COPYRIGHT ⓒ SOONGSIL UNIVERSITY, ALL RIGHTS RESERVED.
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.