Sparsity-based Channel Estimation for RIS-aided MIMO mmWave Communication Systems

Reconfigurable intelligent surfaces (RIS) are emerging as a promising technology for 6 G networks due to their ability to shape the radio propagation environment. This ability helps to overcome propagation challenges, such as high path loss, absorption loss, etc., that are particularly needed at millimetre wave (mmWave) bands. Thus, RIS can enhance the capacity of next-generation mmWave communication networks. However, one critical aspect in the design of RIS-aided systems is channel estimation, as it involves estimating two channels (i.e., base stationRIS and RIS-user terminal) separately based on the compound channel observed by the receiver. In this paper, we address this problem by proposing an algorithm that estimates both channels separately by leveraging the advantages of sparsity of the mmWave channel. Specifically, the proposed algorithm uses the parallel factor (PARAFAC) decomposition of the tensor, which is constructed using the received signal matrices observed under different RIS phase shift configurations. Our numerical analysis shows that the proposed algorithm provides significantly smaller normalized mean square error (NMSE) than the widely used alternating least squares (ALS) algorithm, particularly when the number of RIS phase shift configurations is smaller than the number of RIS elements.