Channel Estimation for RIS-Assisted MU-MIMO Communication System with Low-Resolution DACs/ADCs

Reconfigurable Intelligent Surface (RIS) is considered as a promising new technology, which provides an energy-efficient and low-cost method to solve the problem of communication link congestion. In this paper, the channel estimation of downlink RIS assisted MU-MIMO communication system is studied. In order to reduce the implementation cost and power consumption, a low-resolution analog-to-digital converters/digital-to-analog converters (ADCs/DACs) is configured at the base station (BS). Based on the common sparsity in cascaded channels, we propose a subspace-based joint sparse matrix recovery algorithm for channel estimation in a multi-user scenario. The goal of this algorithm is to achieve accurate channel estimation while minimizing the training overhead and ensuring robustness. Firstly, we utilize the AoD subspace estimation method to characterize the common AoD subspace and project the received signals onto the estimated subspace. Finally, we propose a multi-user joint sparse matrix recovery algorithm considering the common channel between base station and RIS. Simulation results verify the effectiveness of the proposed algorithm.