{"ID":2883088,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08663","arxiv_id":"2508.08663","title":"Sparse Near-Field Channel Estimation for XL-MIMO via Adaptive Filtering","abstract":"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.","short_abstract":"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. Assumin...","url_abs":"https://arxiv.org/abs/2508.08663","url_pdf":"https://arxiv.org/pdf/2508.08663v1","authors":"[\"Vidya Bhasker Shukla\",\"Italo Atzeni\"]","published":"2025-08-12T06:05:24Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}