Triple-Structured Compressive Sensing-Based Channel Estimation for RIS-Aided MU-MIMO Systems

Reconfigurable intelligent surface (RIS) has been recognized as a potential technology for 5G beyond and attracted tremendous research attention. However, channel estimation RIS-aided systems is still critical challenge due to the excessive amount of parameters in cascaded channel. The existing compressive sensing (CS)-based RIS schemes only adopt incomplete sparsity, which induces redundant pilot consumption. In this paper, we analyze exploit specific triple-structured sparsity channel, i.e., common column structured row after offset compensation offsets among all users. Furthermore, novel on-grid Multi-user Triple-Structured-Compressive-Sensing simultaneous orthogonal matching pursuit (MTSCS-SOMP) algorithm along with an enhanced super-resolution (gridless) generalized iterative reweighted (MTSCS-IR) scheme are successively proposed. former practical can be easily employed low computational complexity, latter further proposed handle severe power leakage problem encountered mmWave estimations. Besides, extend property algorithms from uniform linear array (ULA) configuration planar (UPA), by transforming matrix tensor. Simulation results show that our approaches significantly reduce overhead over 50% achieve performance on accuracy.