TY - JOUR
T1 - Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
AU - Elbir, Ahmet M.
AU - Papazafeiropoulos, Anastasios
AU - Kourtessis, Pandelis
AU - Chatzinotas, Symeon
AU - Senior, John
N1 - © 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.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Deep learning
KW - channel estimation
KW - large intelligent surfaces
KW - massive MIMO
UR - http://www.scopus.com/inward/record.url?scp=85091179272&partnerID=8YFLogxK
U2 - 10.1109/LWC.2020.2993699
DO - 10.1109/LWC.2020.2993699
M3 - Article
VL - 9
SP - 1447
EP - 1451
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
SN - 2162-2337
IS - 9
M1 - 9090876
ER -