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Reconfigurable intelligent surface (RIS) has been recognized as a potentialtechnology for 5G beyond and attracted tremendous research attention. However,channel estimation in RIS-aided system is still a critical challenge due to theexcessive amount of parameters in cascaded channel. The existing compressivesensing (CS)-based RIS estimation schemes only adopt incomplete sparsity, whichinduces redundant pilot consumption. In this paper, we exploit the specifictriple-structured sparsity of the cascaded channel, i.e., the common columnsparsity, structured row sparsity after offset compensation and the commonoffsets among all users. Then a novel multi-user joint estimation algorithm isproposed. Simulation results show that our approach can significantly reducepilot overhead in both ULA and UPA scenarios.

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