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Enabling technologies for AI empowered 6G massive radio access networks

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dc.contributor.authorShahjalal, Md.-
dc.contributor.authorKim, Woojun-
dc.contributor.authorKhalid, Waqas-
dc.contributor.authorMoon, Seokjae-
dc.contributor.authorKhan, Murad-
dc.contributor.authorLiu, ShuZhi-
dc.contributor.authorLim, Suhyeon-
dc.contributor.authorKim, Eunjin-
dc.contributor.authorYun, Deok-Won-
dc.contributor.authorLee, Joohyun-
dc.contributor.authorLee, Won-Cheol-
dc.contributor.authorHwang, Seung-Hoon-
dc.contributor.authorKim, Dongkyun-
dc.contributor.authorLee, Jang-Won-
dc.contributor.authorYu, Heejung-
dc.contributor.authorSung, Youngchul-
dc.contributor.authorJang, Yeong Min-
dc.date.accessioned2023-05-03T09:30:28Z-
dc.date.available2023-05-03T09:30:28Z-
dc.date.issued2023-06-
dc.identifier.issn2405-9595-
dc.identifier.issn2405-9595-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/112455-
dc.description.abstractPredictably, the upcoming six generation (6G) networks demand ultra-massive interconnectivity comprising densely congested sustainable small-to-tiny networks. The conventional radio access network (RAN) will be redesigned to provide the necessary intelligence in all areas to meet required network flexibility, full coverage, and massive access. In this respect, this paper focuses on intelligent massive RAN (mRAN) architecture and key technologies fulfilling the requirements. Particularly, we investigate potential AI algorithms for network and resource management issues in 6G mRAN. Furthermore, we summarize the research issues in edge technologies and physical layer intelligence on 6G network architecture. © 2022 The Author(s)-
dc.format.extent15-
dc.language영어-
dc.language.isoENG-
dc.publisherELSEVIER-
dc.titleEnabling technologies for AI empowered 6G massive radio access networks-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.1016/j.icte.2022.07.002-
dc.identifier.scopusid2-s2.0-85135172179-
dc.identifier.wosid001025312000001-
dc.identifier.bibliographicCitationICT EXPRESS, v.9, no.3, pp 341 - 355-
dc.citation.titleICT EXPRESS-
dc.citation.volume9-
dc.citation.number3-
dc.citation.startPage341-
dc.citation.endPage355-
dc.type.docTypeArticle-
dc.identifier.kciidART002977344-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaTelecommunications-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryTelecommunications-
dc.subject.keywordPlusINTELLIGENT REFLECTING SURFACE-
dc.subject.keywordPlusLEARNING-BASED APPROACH-
dc.subject.keywordPlusRESOURCE-MANAGEMENT-
dc.subject.keywordPlusTRAFFIC PREDICTION-
dc.subject.keywordPlusCELLULAR NETWORKS-
dc.subject.keywordPlusPOWER ALLOCATION-
dc.subject.keywordPlusMIMO SYSTEMS-
dc.subject.keywordPlusDEEP-
dc.subject.keywordPlusCOMMUNICATION-
dc.subject.keywordPlusCHALLENGES-
dc.subject.keywordAuthor6G-
dc.subject.keywordAuthorAI-assisted networking-
dc.subject.keywordAuthorEdge AI-
dc.subject.keywordAuthorMassive radio access networks-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S2405959522000996?via%3Dihub-
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ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
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