{"ID":5935679,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T02:10:06.972658124Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03448","arxiv_id":"2607.03448","title":"ISTA-Based Joint Dictionary Learning and Channel Estimation for XL-MIMO Systems","abstract":"Channel estimation in extra-large multiple-input multiple-output systems is challenging due to near-field propagation, where the array response depends on both the angle and distance of the propagation paths. Existing near-field channel estimation methods typically rely either on fixed angle-distance grids, which suffer from grid mismatch effects, or on multi-stage refinement procedures with increased computational complexity. To address these limitations, this paper proposes the \\textit{dictionary-learning iterative soft-thresholding algorithm (DL-ISTA)}, a method for joint near-field dictionary learning and channel estimation based on the iterative soft-thresholding algorithm. The proposed method jointly estimates the sparse channel coefficients and the continuous angle-distance parameters through alternating optimization, thereby avoiding discretization errors associated with fixed grids. To promote robust convergence, the angle-distance parameters are initialized using Sobol sequences, which provide uniform coverage of the parameter space. Numerical results show that DL-ISTA outperforms a baseline with comparable computational complexity and attains comparable or better accuracy than a substantially more complex benchmark.","short_abstract":"Channel estimation in extra-large multiple-input multiple-output systems is challenging due to near-field propagation, where the array response depends on both the angle and distance of the propagation paths. Existing near-field channel estimation methods typically rely either on fixed angle-distance grids, which suffe...","url_abs":"https://arxiv.org/abs/2607.03448","url_pdf":"https://arxiv.org/pdf/2607.03448v1","authors":"[\"Arttu Arjas\",\"Italo Atzeni\"]","published":"2026-07-03T16:06:18Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}