Reference : Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
Scientific journals : Article
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/45463
Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
English
Elbir, Ahmet M. []
Papazafeiropoulos, Anastasios []
Kourtessis, Pandelis []
Chatzinotas, Symeon mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
11-May-2020
IEEE Wireless Communications Letters
IEEE Communications Society
9
Sept. 2020
1447-1451
Yes (verified by ORBilu)
2162-2337
2162-2345
Piscataway
NJ
[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.
10.13039/100010663-ERC Project AGNOSTIC
Researchers
http://hdl.handle.net/10993/45463
10.1109/LWC.2020.2993699

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
Deep Channel Learning For Large Intelligent.pdfDeep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO SystemsPublisher postprint520.7 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.