Robust Bayesian learning approach for massive MIMO channel estimation

Abstract

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.