Hierarchical Sparse Prior-Based Channel Estimation for Multi-User Massive MIMO-OFDM Systems Using Unitary AMP

Abstract

Accurate uplink channel estimation is a cornerstone for achieving spatial multiplexing gains in multi-user massive multiple-input multiple-output (MIMO) systems under practical pilot constraints. In this paper, we propose a novel Bayesian inference framework-based unitary approximate message passing (BIF-UAMP) algorithm tailored to the TR 38.901 channel specification. Specifically, a hierarchical prior channel model is formulated in the antenna-delay domain, which jointly exploits the physical support patterns and numerical amplitude variations via a delay-tap-specific Bernoulli-Gaussian first-order Markov chain (BG-MC). Driven by this structured prior, the UAMP rule is sequentially integrated into the three-layer Bayesian architecture to efficiently process the deterministic sensing matrix, effectively mitigating the numerical degradation triggered by ill-conditioned measurements. Simulation results demonstrate that the proposed BIF-UAMP algorithm achieves superior channel estimation accuracy compared with state-of-the-art schemes, while consistently approaching the theoretical Cramér-Rao bound. Show more