Sparse Near-Field Channel Estimation for XL-MIMO via Adaptive Filtering
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912533025128448 |
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| author | Shukla, Vidya Bhasker Atzeni, Italo |
| author_facet | Shukla, Vidya Bhasker Atzeni, Italo |
| contents | Extremely large-scale multiple-input multiple-output (XL-MIMO) systems operating at sub-THz carrier frequencies represent a promising solution to meet the demands of next-generation wireless applications. This work focuses on sparse channel estimation for XL-MIMO systems operating in the near-field (NF) regime. Assuming a practical subarray-based architecture, we develop a NF channel estimation framework based on adaptive filtering, referred to as \textit{polar-domain zero-attracting least mean squares (PD-ZALMS)}. The proposed method achieves significantly superior channel estimation accuracy and lower computational complexity compared with the well-established polar-domain orthogonal matching pursuit. In addition, the proposed PD-ZALMS is shown to outperform the oracle least-squares channel estimator at low-to-moderate signal-to-noise ratio. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sparse Near-Field Channel Estimation for XL-MIMO via Adaptive Filtering Shukla, Vidya Bhasker Atzeni, Italo Signal Processing Extremely large-scale multiple-input multiple-output (XL-MIMO) systems operating at sub-THz carrier frequencies represent a promising solution to meet the demands of next-generation wireless applications. This work focuses on sparse channel estimation for XL-MIMO systems operating in the near-field (NF) regime. Assuming a practical subarray-based architecture, we develop a NF channel estimation framework based on adaptive filtering, referred to as \textit{polar-domain zero-attracting least mean squares (PD-ZALMS)}. The proposed method achieves significantly superior channel estimation accuracy and lower computational complexity compared with the well-established polar-domain orthogonal matching pursuit. In addition, the proposed PD-ZALMS is shown to outperform the oracle least-squares channel estimator at low-to-moderate signal-to-noise ratio. |
| title | Sparse Near-Field Channel Estimation for XL-MIMO via Adaptive Filtering |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2508.08663 |