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

TL;DR

This paper introduces a novel compressive sensing-based channel estimation method for RIS-aided MU-MIMO systems that leverages triple-structured sparsity to reduce pilot overhead and improve estimation efficiency.

Contribution

It proposes a new multi-user joint estimation algorithm exploiting triple-structured sparsity in cascaded channels, addressing the limitations of previous incomplete sparsity methods.

Findings

Significantly reduces pilot overhead in ULA and UPA scenarios.

Effectively exploits triple-structured sparsity for improved channel estimation.

Demonstrates superior performance over existing schemes in simulations.

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.