Compressed channel estimation for RIS-assisted wireless systems: An efficient sparse recovery algorithm

Abstract

The reconfigurable intelligent surface (RIS) technology plays an essential role in controlling the propagation environment of wireless systems, specifically millimeter wave (mmWave) communication systems. However, a considerable number of pilot signals should be provided at the base station (BS) side for channel estimation due to the significant passive elements installed in the RIS. In this paper, we propose a novel sparse recovery algorithm by exploiting the row, common, and individual sparse elements of the channel in a new way. Furthermore, as an essential presumption of real environments, a sparse channel with unknown parameters is considered. Then a halting method is introduced to stop the proposed algorithm at the best time. The numerical results demonstrate that the proposed algorithm can alleviate the pilot overhead problem substantially while the mean square error (MSE) still approaches the performance bound.