Publication: Deep learning-aided 6G wireless networks: a comprehensive survey of revolutionary PHY architectures
dc.contributor.department | Department of Electrical and Electronics Engineering | |
dc.contributor.department | Graduate School of Sciences and Engineering | |
dc.contributor.kuauthor | Altun, Ufuk | |
dc.contributor.kuauthor | Başar, Ertuğrul | |
dc.contributor.kuauthor | Doğukan, Ali Tuğberk | |
dc.contributor.kuauthor | Gevez, Yarkın | |
dc.contributor.kuauthor | Özpoyraz, Burak | |
dc.contributor.schoolcollegeinstitute | College of Engineering | |
dc.contributor.schoolcollegeinstitute | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
dc.date.accessioned | 2024-11-09T12:38:55Z | |
dc.date.issued | 2022 | |
dc.description.abstract | Deep 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.fulltext | YES | |
dc.description.indexedby | WOS | |
dc.description.indexedby | Scopus | |
dc.description.openaccess | YES | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | N/A | |
dc.description.sponsorship | The 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.version | Publisher version | |
dc.description.volume | 3 | |
dc.identifier.doi | 10.1109/OJCOMS.2022.3210648 | |
dc.identifier.eissn | 2644-125X | |
dc.identifier.embargo | NO | |
dc.identifier.filenameinventoryno | IR04028 | |
dc.identifier.quartile | N/A | |
dc.identifier.scopus | 2-s2.0-85139404739 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/2023 | |
dc.identifier.wos | 870287600002 | |
dc.keywords | 6G mobile communication | |
dc.keywords | 5G mobile communication | |
dc.keywords | Modulation | |
dc.keywords | Artificial intelligence | |
dc.keywords | Wireless networks | |
dc.keywords | Wireless communication | |
dc.keywords | Millimeter wave communication | |
dc.keywords | Deep learning | |
dc.keywords | 6G | |
dc.keywords | Massive multiple-input multiple-output (MIMO) | |
dc.keywords | Multi-carrier (MC) waveform designs | |
dc.keywords | Reconfigurable intelligent surfaces (RIS) | |
dc.keywords | Physical layer (PHY) security | |
dc.language.iso | eng | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
dc.relation.grantno | NA | |
dc.relation.ispartof | IEEE Open Journal of the Communications Society | |
dc.relation.uri | http://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/10908 | |
dc.subject | Engineering | |
dc.subject | Electrical and electronic | |
dc.subject | Telecommunications | |
dc.title | Deep learning-aided 6G wireless networks: a comprehensive survey of revolutionary PHY architectures | |
dc.type | Journal Article | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Başar, Ertuğrul | |
local.contributor.kuauthor | Özpoyraz, Burak | |
local.contributor.kuauthor | Doğukan, Ali Tuğberk | |
local.contributor.kuauthor | Gevez, Yarkın | |
local.contributor.kuauthor | Altun, Ufuk | |
local.publication.orgunit1 | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
local.publication.orgunit1 | College of Engineering | |
local.publication.orgunit2 | Department of Electrical and Electronics Engineering | |
local.publication.orgunit2 | Graduate School of Sciences and Engineering | |
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relation.isOrgUnitOfPublication.latestForDiscovery | 21598063-a7c5-420d-91ba-0cc9b2db0ea0 | |
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