Channel Estimation for Irregular Subarrayed RIS-Aided mmwave Communications

Channel estimation is essential for reconfigurable intelligent surface (RIS)-aided wireless communications. Irregular RIS structures can provide more design degrees of freedom to enhance system performance. In this paper, we investigate the channel estimation problem for an irregular subarrayed RIS-aided millimeter wave (mmWave) system. Specifically, we formulate the channel estimation problem as a sparse recovery problem by exploiting the sparsity of the mmWave channel, which can be effectively solved by the proposed two-stage algorithm. In the first stage, the common angles of arrival (AoAs) at the base station (BS) are estimated using the discrete Fourier transform (DFT) method. In the second stage, the estimated AoA information is used to decouple signals from different paths, thus leading to the user-specific subchannel estimation problem. Moreover, effective sparse angles are sampled within the sine and cosine domains to reduce the number of channel parameters. Then, we apply the expectation-maximization-based variational Bayesian inference (EM-VBI) method to estimate these parameters. Furthermore, taking into account practical calibration errors among sub-RISs, we propose to estimate channel parameters and auxiliary error parameters simultaneously, along with an effective method to extract the original error information from these auxiliary parameters. Simulation results demonstrate the superiority of the proposed algorithms over existing benchmark schemes in terms of the channel estimation accuracy and the calibration accuracy of intersubarray errors.