Robust Bayesian learning approach for massive MIMO channel estimation

• Published in: Signal processing 2020-03, Vol.168, p.107345, Article 107345
• Main Authors: Dai, Jisheng, Zhou, Lei, Chang, Chunqi, Xu, Weichao
• Format: Article
• Language: English
• Subjects: Channel estimation; Impulsive noise; Massive MIMO; Robust estimation; Sparse Bayesian learning (SBL); Variational Bayesian inference (VBI)
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2019.107345

•Massive MIMO channel estimation is sensitive to outliers.•Adopt VBI to separate signal-of-interest and impulsive noise.•Provide an improved two-stage hierarchical prior to exploit sparsity property of channel and impulsive noise.•Bring a computational complexity reduction and achieve better channel estimation accuracy. 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.