{"ID":2832829,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.04470","arxiv_id":"2512.04470","title":"Joint Low-Rank and Sparse Bayesian Channel Estimation for Ultra-Massive MIMO Communications","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.","short_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,...","url_abs":"https://arxiv.org/abs/2512.04470","url_pdf":"https://arxiv.org/pdf/2512.04470v1","authors":"[\"Jianghan Ji\",\"Cheng-Xiang Wang\",\"Shuaifei Chen\",\"Chen Huang\",\"Xiping Wu\",\"Emil Björnson\"]","published":"2025-12-04T05:22:57Z","proceeding":"cs.IT","tasks":"[\"cs.IT\",\"eess.SP\"]","methods":"[]","has_code":false}