Regularized Sparse Bayesian Learning Based Channel Estimation for RIS-Assisted Wireless Communication System

Reconfigurable intelligent surface (RIS) holds great promise as communication aid which is capable of controlling the electromagnetic propagation environment by adjusting the phase shift of reflective elements. However, in RIS-assisted multi-user wireless communication systems, channel estimation is challenging due to the inclusion of a large number of passive reflective elements within the RIS. To address this problem, we take advantage of the sparsity inherent in multi-user cascade channels and propose a novel cascade channel estimation strategy with low pilot overhead. Specifically, inspired by matrix vectorization and total variational regularization, we estimate the cascaded sparse channels by following Sparse Bayesian Learning (SBL) inference after vectorizing the channel matrix. By introducing regularization to virtual angle-domain sparse channel hyperparameter priors during hyperparameter updates, which penalize non-zero and zero regions of sparse channels differently, thereby enhance the sparseness objective exploited by SBL, this innovation results in a significant reduction in pilot overhead. We then perform SBL inference by updating hyperparameters in parallel using an Expectation-Maximization-based segment alternating optimization method. Simulation results demonstrate the effectiveness of our proposed algorithm that it can substantially reduce the pilot overhead.