Joint Low-Rank and Sparse Bayesian Channel Estimation for Ultra-Massive MIMO Communications
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914179667984384 |
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| author | Ji, Jianghan Wang, Cheng-Xiang Chen, Shuaifei Huang, Chen Wu, Xiping Björnson, Emil |
| author_facet | Ji, Jianghan Wang, Cheng-Xiang Chen, Shuaifei Huang, Chen Wu, Xiping Björnson, Emil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04470 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Low-Rank and Sparse Bayesian Channel Estimation for Ultra-Massive MIMO Communications Ji, Jianghan Wang, Cheng-Xiang Chen, Shuaifei Huang, Chen Wu, Xiping Björnson, Emil Information Theory Signal Processing 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. |
| title | Joint Low-Rank and Sparse Bayesian Channel Estimation for Ultra-Massive MIMO Communications |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2512.04470 |