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

• URL: http://arxiv.org/abs/2001.11085v3
• Date: Wed, 13 May 2020 10:50:37 GMT
• Title: Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
• Authors: Ahmet M. Elbir, A Papazafeiropoulos, P. Kourtessis, and S. Chatzinotas
• Abstract summary: This letter presents the first work introducing a deep learning framework for channel estimation in large intelligent surface (LIS) 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.
• Score: 3.2898781698366717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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