Blind Channel Estimation and Data Detection for Near-Field XL-MIMO Systems
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910257236672512 |
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| author | Safari, Maral Atzeni, Italo |
| author_facet | Safari, Maral Atzeni, Italo |
| contents | Future wireless systems are expected to employ extremely large-scale multiple-input multiple-output (XL-MIMO) arrays at high carrier frequencies, where near-field propagation makes the channel depend jointly on angle and distance. The resulting short coherence intervals make channel state information acquisition challenging, motivating blind channel estimation and data detection (B-CE-DD). In this paper, we propose a two-stage B-CE-DD framework for uplink near-field XL-MIMO systems. First, we formulate the problem as the recovery of user-specific rank-one channel-data products from a superimposed received signal using a polar-domain sparse channel model and a low-dimensional data subspace model. Building on this formulation, we develop an on-grid blind orthogonal matching pursuit (B-OMP) algorithm that exploits polar-domain sparsity to iteratively identify the dominant angle-distance components and estimate the corresponding channel-data products, followed by an off-grid refinement stage based on block-coordinate descent (BCD) that optimizes the angle and distance parameters in the continuous polar domain. Numerical results show that the proposed B-CE-DD framework combining B-OMP and BCD significantly improves the symbol error rate compared with a pilot-based baseline employing zero-forcing beamforming, particularly at low signal-to-noise ratio and when the number of data symbols is small relative to the length of the coherence interval. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26249 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Blind Channel Estimation and Data Detection for Near-Field XL-MIMO Systems Safari, Maral Atzeni, Italo Signal Processing Future wireless systems are expected to employ extremely large-scale multiple-input multiple-output (XL-MIMO) arrays at high carrier frequencies, where near-field propagation makes the channel depend jointly on angle and distance. The resulting short coherence intervals make channel state information acquisition challenging, motivating blind channel estimation and data detection (B-CE-DD). In this paper, we propose a two-stage B-CE-DD framework for uplink near-field XL-MIMO systems. First, we formulate the problem as the recovery of user-specific rank-one channel-data products from a superimposed received signal using a polar-domain sparse channel model and a low-dimensional data subspace model. Building on this formulation, we develop an on-grid blind orthogonal matching pursuit (B-OMP) algorithm that exploits polar-domain sparsity to iteratively identify the dominant angle-distance components and estimate the corresponding channel-data products, followed by an off-grid refinement stage based on block-coordinate descent (BCD) that optimizes the angle and distance parameters in the continuous polar domain. Numerical results show that the proposed B-CE-DD framework combining B-OMP and BCD significantly improves the symbol error rate compared with a pilot-based baseline employing zero-forcing beamforming, particularly at low signal-to-noise ratio and when the number of data symbols is small relative to the length of the coherence interval. |
| title | Blind Channel Estimation and Data Detection for Near-Field XL-MIMO Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.26249 |