Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
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Publication:6333753
arXiv2001.11085MaRDI QIDQ6333753
Author name not available (Why is that?)
Publication date: 29 January 2020
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.
Has companion code repository: https://github.com/meuseabe/deepchannellearning4ris
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