Article (Scientific journals)
Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
Elbir, Ahmet M.; Papazafeiropoulos, Anastasios; Kourtessis, Pandelis et al.
2020In IEEE Wireless Communications Letters, 9 (Sept. 2020), p. 1447-1451
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Abstract :
[en] 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.
Disciplines :
Computer science
Author, co-author :
Elbir, Ahmet M.
Papazafeiropoulos, Anastasios
Kourtessis, Pandelis
Chatzinotas, Symeon  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
External co-authors :
yes
Language :
English
Title :
Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
Publication date :
11 May 2020
Journal title :
IEEE Wireless Communications Letters
ISSN :
2162-2345
Publisher :
IEEE Communications Society, Piscataway, United States - New Jersey
Volume :
9
Issue :
Sept. 2020
Pages :
1447-1451
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
Funders :
10.13039/100010663-ERC Project AGNOSTIC
Available on ORBilu :
since 13 January 2021

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