Adaptive Structured Sparse Bayesian Learning for Near-Field Non-Stationary Channel Estimation in XL-MIMO Systems
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911587021881344 |
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| author | Feng, Qingxia Fang, Pan Hua, Meng Li, Chunguo Huang, Yongming Yang, Luxi |
| author_facet | Feng, Qingxia Fang, Pan Hua, Meng Li, Chunguo Huang, Yongming Yang, Luxi |
| contents | Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key enabler for sixth-generation (6G) communications. However, near-field channel estimation is particularly challenging due to spherical-wave propagation and spatial non-stationarity. To tackle this challenge, we propose a structured sparse Bayesian learning framework with adaptive dictionary updating for near-field non-stationary channel estimation. Specifically, the proposed method iteratively updates the distance parameters within an adaptive dictionary, thereby enhancing the representation capability without increasing the dictionary size. Moreover, we develop a hierarchical prior model that jointly captures polar-domain sparsity and structured dependency, enabling efficient Bayesian inference. Simulation results demonstrate that the proposed approach outperforms existing polar-domain dictionary-based methods while achieving low dictionary overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11039 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Structured Sparse Bayesian Learning for Near-Field Non-Stationary Channel Estimation in XL-MIMO Systems Feng, Qingxia Fang, Pan Hua, Meng Li, Chunguo Huang, Yongming Yang, Luxi Signal Processing Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key enabler for sixth-generation (6G) communications. However, near-field channel estimation is particularly challenging due to spherical-wave propagation and spatial non-stationarity. To tackle this challenge, we propose a structured sparse Bayesian learning framework with adaptive dictionary updating for near-field non-stationary channel estimation. Specifically, the proposed method iteratively updates the distance parameters within an adaptive dictionary, thereby enhancing the representation capability without increasing the dictionary size. Moreover, we develop a hierarchical prior model that jointly captures polar-domain sparsity and structured dependency, enabling efficient Bayesian inference. Simulation results demonstrate that the proposed approach outperforms existing polar-domain dictionary-based methods while achieving low dictionary overhead. |
| title | Adaptive Structured Sparse Bayesian Learning for Near-Field Non-Stationary Channel Estimation in XL-MIMO Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.11039 |