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.
@article{arxiv.2001.11085,
title = {Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems},
author = {Ahmet M. Elbir and A Papazafeiropoulos and P. Kourtessis and S. Chatzinotas},
journal= {arXiv preprint arXiv:2001.11085},
year = {2020}
}
Comments
Accepted paper in IEEE Wireless Communications Letters