Hierarchical-Block Sparse Bayesian Learning for Spatial Non-Stationary Massive MIMO Channel Estimation

This letter provides a novel hierarchical-block channel estimation scheme for massive multiple-input multiple-output (MIMO) systems based on Bayesian learning frameworks. Specifically, considering the spatial non-stationarity of massive MIMO channels introduced by a large-scale antenna array, we first exploit a hierarchical-block prior to characterize the delay-domain sparse channel structure across the antenna array and then develop an iterative Bayesian learning scheme to infer the channel vector as well as its hyperparameters associated with the hierarchical-block prior. Meanwhile, the Cramer-Rao bound (CRB) is derived as a reference line. To evaluate our proposed scheme, we modify the standardized 3GPP channel model with birth-death process to capture the spatial non-stationarity of practical massive MIMO channels. Simulation results demonstrate that our proposed scheme can recover channels effectively and attain a significant performance improvement over comparison methods.

Automated summary

This paper proposes a novel hierarchical-block channel estimation scheme for massive MIMO systems based on Bayesian learning frameworks. By characterizing the delay-domain sparse channel structure and inferring the channel vector and associated hyperparameters using an iterative Bayesian learning scheme, our proposed scheme outperforms comparison methods in terms of channel recovery and performance improvement.