{"ID":2887976,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00626","arxiv_id":"2508.00626","title":"Deep Learning-Based Rate-Adaptive CSI Feedback for Wideband XL-MIMO Systems in the Near-Field Domain","abstract":"Accurate and efficient channel state information (CSI) feedback is crucial for unlocking the substantial spectral efficiency gains of extremely large-scale MIMO (XL-MIMO) systems in future 6G networks. However, the combination of near-field spherical wave propagation and frequency-dependent beam split effects in wideband scenarios poses significant challenges for CSI representation and compression. This paper proposes WideNLNet-CA, a rate-adaptive deep learning framework designed to enable efficient CSI feedback in wideband near-field XL-MIMO systems. WideNLNet-CA introduces a lightweight encoder-decoder architecture with multi-stage downsampling and upsampling, incorporating computationally efficient residual blocks to capture complex multi-scale channel features with reduced overhead. A novel compression ratio adaptive module with feature importance estimation is introduced to dynamically modulate feature selection based on target compression ratios, enabling flexible adaptation across a wide range of feedback rates using a single model. Evaluation results demonstrate that WideNLNet-CA consistently outperforms existing compressive sensing and deep learning-based works across various compression ratios and bandwidths, while maintaining fast inference and low model storage requirements.","short_abstract":"Accurate and efficient channel state information (CSI) feedback is crucial for unlocking the substantial spectral efficiency gains of extremely large-scale MIMO (XL-MIMO) systems in future 6G networks. However, the combination of near-field spherical wave propagation and frequency-dependent beam split effects in wideba...","url_abs":"https://arxiv.org/abs/2508.00626","url_pdf":"https://arxiv.org/pdf/2508.00626v1","authors":"[\"Zhenyu Liu\",\"Yi Ma\",\"Rahim Tafazolli\"]","published":"2025-08-01T13:38:38Z","proceeding":"cs.IT","tasks":"[\"cs.IT\",\"eess.SP\"]","methods":"[]","has_code":false}