Related papers: Deep Learning Based RIS Channel Extrapolation with Element-grouping

Deep Learning Based RIS Channel Extrapolation with Element-grouping

URL: http://arxiv.org/abs/2105.06850v1
Date: Fri, 14 May 2021 14:24:54 GMT
Title: Deep Learning Based RIS Channel Extrapolation with Element-grouping
Authors: Shunbo Zhang, Shun Zhang, Feifei Gao, Jianpeng Ma, Octavia A. Dobre
Abstract summary: We consider the acquisition of the cascaded channels, which is a challenging task due to the massive number of passive RIS elements. We adopt the element-grouping strategy, where each element in one group shares the same reflection coefficient and is assumed to have the same channel condition. We analyze the channel interference caused by the element-grouping strategy and further design two deep learning based networks.
Score: 61.18715079535163
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks. In this letter, we consider the acquisition of the cascaded channels, which is a challenging task due to the massive number of passive RIS elements. To reduce the pilot overhead, we adopt the element-grouping strategy, where each element in one group shares the same reflection coefficient and is assumed to have the same channel condition. We analyze the channel interference caused by the element-grouping strategy and further design two deep learning based networks. The first one aims to refine the partial channels by eliminating the interference, while the second one tries to extrapolate the full channels from the refined partial channels. We cascade the two networks and jointly train them. Simulation results show that the proposed scheme provides significant gain compared to the conventional element-grouping method without interference elimination.

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