dc.contributor.author | Elbir, Ahmet M. | |
dc.contributor.author | Papazafeiropoulos, Anastasios | |
dc.contributor.author | Kourtessis, Pandelis | |
dc.contributor.author | Chatzinotas, Symeon | |
dc.contributor.author | Senior, John | |
dc.date.accessioned | 2020-10-14T00:09:17Z | |
dc.date.available | 2020-10-14T00:09:17Z | |
dc.date.issued | 2020-09 | |
dc.identifier.citation | Elbir , 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.issn | 2162-2345 | |
dc.identifier.other | ORCID: /0000-0003-1841-6461/work/82133299 | |
dc.identifier.other | ORCID: /0000-0002-4881-560X/work/82133239 | |
dc.identifier.uri | http://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.abstract | This 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.extent | 5 | |
dc.format.extent | 579254 | |
dc.language.iso | eng | |
dc.relation.ispartof | IEEE Wireless Communications Letters | |
dc.subject | Deep learning | |
dc.subject | channel estimation | |
dc.subject | large intelligent surfaces | |
dc.subject | massive MIMO | |
dc.subject | Control and Systems Engineering | |
dc.subject | Electrical and Electronic Engineering | |
dc.title | Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems | en |
dc.contributor.institution | Centre for Engineering Research | |
dc.contributor.institution | Communications and Intelligent Systems | |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | Department of Engineering and Technology | |
dc.contributor.institution | Optical Networks | |
dc.contributor.institution | Centre for Computer Science and Informatics Research | |
dc.contributor.institution | Office of the Vice-Chancellor | |
dc.contributor.institution | SPECS Deans Group | |
dc.description.status | Peer reviewed | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85091179272&partnerID=8YFLogxK | |
rioxxterms.versionofrecord | 10.1109/LWC.2020.2993699 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |