Reconfigurable intelligent surface (RIS) has been recognized as a potential technology for 5G beyond and attracted tremendous research attention. However, channel estimation in RIS-aided system is still a critical challenge due to the excessive amount of parameters in cascaded channel. The existing compressive sensing (CS)-based RIS estimation schemes only adopt incomplete sparsity, which induces redundant pilot consumption. In this paper, we exploit the specific triple-structured sparsity of the cascaded channel, i.e., the common column sparsity, structured row sparsity after offset compensation and the common offsets among all users. Then a novel multi-user joint estimation algorithm is proposed. Simulation results show that our approach can significantly reduce pilot overhead in both ULA and UPA scenarios.
@article{arxiv.2108.13765,
title = {Triple-Structured Compressive Sensing-based Channel Estimation for RIS-aided MU-MIMO Systems},
author = {Xu Shi and Jintao Wang and Guozhi Chen and Jian Song},
journal= {arXiv preprint arXiv:2108.13765},
year = {2021}
}