Accurate channel estimation plays a pivotal role in realizing the passive beamforming gain of reconfigurable intelligent surfaces (RIS). Multi-user RIS-assisted channels exhibit a common sparse structure in the angular domain, which offers the potential to enhance channel estimation performance. Nonetheless, most existing methods simply partition the multi-user channel estimation problem into multiple sub-problems, which regrettably neglect the exploitation of the shared property across different users. In this paper, we formulate a unified multi-user channel estimation problem and propose a computationally efficient message passing approach to jointly extract the common sparsity. We first introduce a new sparse Bayesian learning (SBL) framework for joint multi-user RIS-assisted channel estimation, where some auxiliary variables and Dirac delta distributions are introduced to handle the intricate interplay of numerous unknown variables within the joint sparse channel representation. Subsequently, we devise a reassembled message passing algorithm for the associated joint Bayesian inference, incorporating a three-stage expected propagation approximation (EPA) procedure along with a novel reassembling technology to facilitate variable decoupling and ensure reliable sparse signal recovery. Simulation results demonstrate the superiority of the proposed algorithm over the state-of-the-art counterparts.

Intelligent reflecting surface (IRS) is deemed as a potential technology for future communications due to its adaptive enhancement for the propagation environment. To achieve the passive beamforming gain of IRS, accurate channel state information (CSI) is essential but practically challenging since its massive passive reflecting elements have no transmitting/receiving capability. This paper presents a new channel estimation problem formulation for IRS-assisted orthogonal frequency division multiplexing (OFDM) systems, where the channel sparsity is exploited in the time-domain. Considering the surfaces are physically close to each other, we further utilize the common sparsity among the different sub-surfaces and automatically cluster them into several groups by introducing a Dirichlet process (DP)-based clustering model. Then, a DP-based variational Bayesian inference (VBI) framework is proposed to jointly estimate the channel and cluster the sub-surfaces, which is expected to significantly improve the channel estimation performance. Moreover, a novel decoupling trick is combined into the VBI framework to efficiently handle the coupling effect brought by the reflection coefficients, as well as facilitate the Bayesian inference. Simulation results verify the effectiveness of the proposed channel estimation scheme and show its significant performance improvement over various benchmark schemes.

This paper addresses the problem of massive multiple-input multiple-output (MIMO) channel estimation in the presence of impulsive noise. In the literature, a sparse Bayesian learning (SBL) approach for outlier-resistant direction-of-arrival (DOA) estimation can be tailored to handle this problem. However, it suffers from two major shortcomings: first, it takes the impulsive noise as a part of the unknown signal-of-interest, which brings a high computational complexity due to the larger size of the problem; and second, the assumption that both the signal-of-interest and the impulsive noise have a common sparsity level is not always valid, which could cause a performance loss. To deal with these shortcomings, we resort to the variational Bayesian inference (VBI) methodology to separate effects from the signal-of-interest and the impulsive noise. Then, we introduce an improved two-stage hierarchical prior to enforce sparsity while guarantee a denser impulsive noise over the signal-of-interest simultaneously. Due to adopting the VBI separation and the new sparsity prior, our method can bring a considerable computational complexity reduction and achieve better channel estimation accuracy. Simulation results reveal substantial performance improvement over the existing methods.