Joint Low-Rank and Sparse Bayesian Channel Estimation for Ultra-Massive MIMO Communications Article Swipe

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Jianghan Ji , Cheng‐Xiang Wang , Shuaifei Chen , Chen Huang , Xiping Wu , Emil Björnson ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1109/lcomm.2025.3637318 · OA: W4416707307

This letter investigates channel estimation for ultra-massive multiple-input multiple-output (MIMO) communications. We propose a joint low-rank and sparse Bayesian estimation (LRSBE) algorithm for spatial non-stationary ultra-massive channels by exploiting the low-rankness and sparsity in the beam domain. Specifically, the channel estimation integrates sparse Bayesian learning and soft-threshold gradient descent within the expectation-maximization framework. Simulation results show that the proposed algorithm significantly outperforms the state-of-the-art alternatives under different signal-to-noise ratio conditions in terms of estimation accuracy and overall complexity.