Sparsity Adaptive RIS in MIMO Wireless Communication System

Reconfigurable intelligent surfaces (RIS), a novel and developing wireless communication technology, has drawn a lot of interest of researchers. RIS consists of a large number of antenna arrays that require high processing power. However, the signals involved in RIS are sparse in some domains. Compressive sensing (CS) could be an appropriate choice to deal with these limits. It can recover sparse or compressed signals with fewer measurements than conventional approaches. In traditional CS algorithms, the accuracy of the recovery signal depends on the sparsity level, as sparsity increases, more accurate signal is recovered. In previous research, structured sparsity was exploited to develop a compressive sensing channel recovery algorithm. In this contribution, novel sparsity adaptive RIS have been proposed. The sparsity level is increased by adjusting the size of RIS and transmitting antennas according to the number of users. The numerical results show that sparsity can be increased by varying the sizes of RIS. In this way, better performance in normalized mean square error (NMSE) can be obtained.