Publication:
Deep learning-aided 6G wireless networks: a comprehensive survey of revolutionary PHY architectures

dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorAltun, Ufuk
dc.contributor.kuauthorBaşar, Ertuğrul
dc.contributor.kuauthorDoğukan, Ali Tuğberk
dc.contributor.kuauthorGevez, Yarkın
dc.contributor.kuauthorÖzpoyraz, Burak
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2024-11-09T12:38:55Z
dc.date.issued2022
dc.description.abstractDeep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising physical layer concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output systems, sophisticated multi-carrier waveform designs, reconfigurable intelligent surface-empowered communications, and physical layer security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DL-based multiple-input multiple-output by sharing user-friendly code snippets, which might be useful for interested readers.
dc.description.fulltextYES
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipThe authors would like to thank Vestel Elektronik Sanayi ve Ticaret A.S. for their financial support of this article under Vestel Elektronik Sanayi ve Ticaret A. S. and Koc University-Industry Cooperation Project No. 119C157.
dc.description.versionPublisher version
dc.description.volume3
dc.identifier.doi10.1109/OJCOMS.2022.3210648
dc.identifier.eissn2644-125X
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR04028
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-85139404739
dc.identifier.urihttps://hdl.handle.net/20.500.14288/2023
dc.identifier.wos870287600002
dc.keywords6G mobile communication
dc.keywords5G mobile communication
dc.keywordsModulation
dc.keywordsArtificial intelligence
dc.keywordsWireless networks
dc.keywordsWireless communication
dc.keywordsMillimeter wave communication
dc.keywordsDeep learning
dc.keywords6G
dc.keywordsMassive multiple-input multiple-output (MIMO)
dc.keywordsMulti-carrier (MC) waveform designs
dc.keywordsReconfigurable intelligent surfaces (RIS)
dc.keywordsPhysical layer (PHY) security
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.grantnoNA
dc.relation.ispartofIEEE Open Journal of the Communications Society
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/10908
dc.subjectEngineering
dc.subjectElectrical and electronic
dc.subjectTelecommunications
dc.titleDeep learning-aided 6G wireless networks: a comprehensive survey of revolutionary PHY architectures
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorBaşar, Ertuğrul
local.contributor.kuauthorÖzpoyraz, Burak
local.contributor.kuauthorDoğukan, Ali Tuğberk
local.contributor.kuauthorGevez, Yarkın
local.contributor.kuauthorAltun, Ufuk
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Electrical and Electronics Engineering
local.publication.orgunit2Graduate School of Sciences and Engineering
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relation.isOrgUnitOfPublication.latestForDiscovery21598063-a7c5-420d-91ba-0cc9b2db0ea0
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