2020/01/31 by Ahmet M. Elbir, A Papazafeiropoulos, P. Kourtessis +1
Engineering · Computer Science · Mathematics · #eess.SP #cs.IT #cs.LG #math.IT
paper · pdf · doi:10.1109/lwc.2020.2993699
published as vol. 9, no. 9, pp. 1447-1451, Sept. 2020 · Accepted paper in IEEE Wireless Communications Letters
arxiv created 2020/05/13 · arxiv updated 2020/09/11
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.