Compressed Sensing Based Joint Sparse Channel Estimation for RIS-Assisted Internet of Things

Reconfigurable intelligent surface (RIS) is deemed as a potential technique for the future of the Internet of Things (IoT) due to its capability of tacking the complex propagation environment using a large number of low-cost passive elements. To benefit from RIS technology, the problem of RIS-assisted channel state information (CSI) acquisition needs to be carefully considered. However, existing channel estimation methods usually incorporated line-of-sigh (LOS) channel into reflected channel together, which is not practical for deploying IoT scenarios. In this paper, we study a RIS-aided channel estimation that jointly exploits the properties of the LOS and reflected channels to provide more accurate CSI. The cascaded RIS channel is modeled based upon the weighted $l$ 1 norm minimization, while a LOS channel is exploited using $f$ 1 norm minimization to sequentially estimate the channel parameters. Moreover, by exploiting gradient descent and the alternating minimization method, a flexible and fast algorithm is developed to provide the feasible solution. Simulation results demonstrate that a RIS-aided MIMO system significantly reduces the active antennas/RF chains compared to other benchmark schemes.