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arXiv · 2108.13765

Triple-Structured Compressive Sensing-based Channel Estimation for RIS-aided MU-MIMO Systems

Xu Shi·Jintao Wang·Guozhi Chen·Jian Song
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Abstract

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.

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Xu Shi, Jintao Wang, Guozhi Chen, Jian Song. 2021-08-31. Triple-Structured Compressive Sensing-based Channel Estimation for RIS-aided MU-MIMO Systems. https://arxiv.org/abs/2108.13765

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