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arXiv · 2001.11085

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

Ahmet M. Elbir·A Papazafeiropoulos·P. Kourtessis·S. Chatzinotas
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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.

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BibTeXRIS

Ahmet M. Elbir, A Papazafeiropoulos, P. Kourtessis, S. Chatzinotas. 2020-01-29. Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems. https://doi.org/10.1109/lwc.2020.2993699

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