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Fast Sparse Bayesian Channel Estimation for Wideband mmWave Systems

In this paper, we propose a fast channel estimation algorithm for wideband millimeter wave (mmWave) massive MIMO systems, where the hybrid precoding architectures are adopted. Stimulated by the joint sparsity of different subcarriers, a distributed compressed sensing-based strategy is presented to reduce the required pilot overhead. Based on a hierarchical channel model, a fast sparse Bayesian learning method, which can drastically release the relaxed evidence low bound, is designed to accelerate the convergence rate. Simulation results verify that the proposed algorithm is capable of achieving substantially higher estimation accuracy and convergence rate as compared to other existing Bayesian channel estimation strategies.