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arXiv · 2512.04470

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

Jianghan Ji·Cheng-Xiang Wang·Shuaifei Chen·Chen Huang·Xiping Wu·Emil Björnson
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Abstract

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.

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Jianghan Ji, Cheng-Xiang Wang, Shuaifei Chen, Chen Huang, Xiping Wu, Emil Björnson. 2025-12-04. Joint Low-Rank and Sparse Bayesian Channel Estimation for Ultra-Massive MIMO Communications. https://doi.org/10.1109/lcomm.2025.3637318

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