Near-Field LoS/NLoS Channel Estimation for RIS-Aided MU-MIMO Systems: Piece-Wise Low-Rank Approximation Approach

Abstract

We investigate the channel estimation problem in a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user multiple-input multiple-output (MU-MIMO) system. It is posited that the channel between the RIS and the base station (BS) comprises a mixed line-of-sight (LoS) and non-line-of-sight (NLoS) near-field channel. The LoS path component is modeled using the geometric free-space propagation assumption, whereas the NLoS path components are characterized by the near-field array response vectors. Existing channel estimation methods exhibit limited performance due to the lack of sparsity or low-rankness in this mixed channel. For the first time, we propose an efficient near-field LoS/NLoS channel estimation method for RIS-assisted MU-MIMO systems through a piece-wise low-rank approximation. Specifically, the effective channel to be estimated is divided into piece-wise effective channels, each exhibiting a low-rank structure. These channels are then estimated via collaborative low-rank approximation. The proposed method is referred to as PW-CLRA. Simulation results substantiate the effectiveness of PW-CLRA.