Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

Ahmet M. Elbir; Anastasios Papazafeiropoulos; Pandelis Kourtessis; Symeon Chatzinotas; John Senior

doi:10.1109/LWC.2020.2993699

Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

Ahmet M. Elbir, Anastasios Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas, John Senior

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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.

Original language	English
Article number	9090876
Pages (from-to)	1447-1451
Number of pages	5
Journal	IEEE Wireless Communications Letters
Volume	9
Issue number	9
Early online date	11 May 2020
DOIs	https://doi.org/10.1109/LWC.2020.2993699
Publication status	Published - Sept 2020

Keywords

Deep learning
channel estimation
large intelligent surfaces
massive MIMO

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Deep Channel Learning For Large Intelligent Surfaces Aided mm_Wave Massive MIMO Systems
© 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.
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Cite this

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AU - Chatzinotas, Symeon

AU - Senior, John

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Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

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Keywords

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