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dc.contributor.authorElbir, Ahmet M.
dc.contributor.authorPapazafeiropoulos, Anastasios
dc.contributor.authorKourtessis, Pandelis
dc.contributor.authorChatzinotas, Symeon
dc.contributor.authorSenior, John
dc.date.accessioned2020-10-14T00:09:17Z
dc.date.available2020-10-14T00:09:17Z
dc.date.issued2020-09
dc.identifier.citationElbir , A M , Papazafeiropoulos , A , Kourtessis , P , Chatzinotas , S & Senior , J 2020 , ' Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems ' , IEEE Wireless Communications Letters , vol. 9 , no. 9 , 9090876 , pp. 1447-1451 . https://doi.org/10.1109/LWC.2020.2993699
dc.identifier.otherPURE: 19513944
dc.identifier.otherPURE UUID: b9b1de89-808f-40e2-8b24-3965b1cc72a6
dc.identifier.otherScopus: 85091179272
dc.identifier.otherORCID: /0000-0003-1841-6461/work/82133299
dc.identifier.urihttp://hdl.handle.net/2299/23259
dc.description© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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
dc.format.extent5
dc.language.isoeng
dc.relation.ispartofIEEE Wireless Communications Letters
dc.subjectDeep learning
dc.subjectchannel estimation
dc.subjectlarge intelligent surfaces
dc.subjectmassive MIMO
dc.subjectControl and Systems Engineering
dc.subjectElectrical and Electronic Engineering
dc.titleDeep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systemsen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.contributor.institutionOptical Networks
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionOffice of the Vice-Chancellor
dc.contributor.institutionSPECS Deans Group
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85091179272&partnerID=8YFLogxK
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1109/LWC.2020.2993699
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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