Deep Channel Learning for Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

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dc.authoridChatzinotas, Symeon/0000-0001-5122-0001
dc.authoridElbir, Ahmet M./0000-0003-4060-3781
dc.authoridPapazafeiropoulos, Anastasios/0000-0003-1841-6461
dc.authoridSenior, John/0000-0002-4881-560X
dc.authoridKourtessis, Pandelis/0000-0003-3392-670X
dc.authorwosidChatzinotas, Symeon/D-4191-2015
dc.authorwosidElbir, Ahmet M./X-3731-2019
dc.contributor.authorElbir, Ahmet M.
dc.contributor.authorPapazafeiropoulos, Anastasios
dc.contributor.authorKourtessis, Pandelis
dc.contributor.authorChatzinotas, Symeon
dc.date.accessioned2021-12-01T18:49:19Z
dc.date.available2021-12-01T18:49:19Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description.abstractThis letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.en_US
dc.description.sponsorshipERC Project AGNOSTICen_US
dc.description.sponsorshipThis work was supported in part by the ERC Project AGNOSTIC.en_US
dc.identifier.doi10.1109/LWC.2020.2993699
dc.identifier.endpage1451en_US
dc.identifier.issn2162-2337
dc.identifier.issn2162-2345
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85091179272en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1447en_US
dc.identifier.urihttps://doi.org/10.1109/LWC.2020.2993699
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10703
dc.identifier.volume9en_US
dc.identifier.wosWOS:000569062000024en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Wireless Communications Lettersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectChannel estimationen_US
dc.subjectMIMO communicationen_US
dc.subjectComplexity theoryen_US
dc.subjectTrainingen_US
dc.subjectMachine learningen_US
dc.subjectSurface wavesen_US
dc.subjectArray signal processingen_US
dc.subjectDeep learningen_US
dc.subjectchannel estimationen_US
dc.subjectlarge intelligent surfacesen_US
dc.subjectmassive MIMOen_US
dc.subjectAntenna Selectionen_US
dc.subjectDesignen_US
dc.titleDeep Channel Learning for Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systemsen_US
dc.typeArticleen_US

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