Channel Estimation for RIS-Aided Multi-User mmWave Systems With Super-Resolution Algorithms

In this paper, we propose a three-stage high-accuracy uplink channel estimation scheme that utilizes super-resolution algorithms for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter-wave (mmWave) multiple-input single-output (MISO) systems. The proposed protocol enhances both estimation accuracy and pilot overhead efficiency. In Stage I, we derive the covariance matrix of the received signal and estimate the common angles-of-arrival (AoAs) at the base station (BS) using super-resolution algorithms. In Stage II, we construct an equivalent multi-snapshot received signal matrix to estimate the cascaded angles-of-departure (AoDs) at the RIS for a typical user. This takes advantage of the invariance of angle information across multiple channel coherence blocks while accounting for varying channel gains. To further reduce noise impact, we apply the minimum mean square error (MMSE) criterion. The full channel state information (CSI) of the typical user is then estimated using a combination of super-resolution algorithms for angle estimation and the least squares (LS) method for gain estimation. In Stage III, we reconstruct the common BS-RIS channel and use the results from Stages I and II to estimate the full CSI for other users, significantly reducing pilot overhead. Simulation results demonstrate that the proposed method outperforms the existing approaches in terms of both angle estimation accuracy and overall performance, while maintaining the same pilot overhead.