{"ID":2870233,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.12748","arxiv_id":"2509.12748","title":"NEFT: A Unified Transformer Framework for Efficient Near-Field CSI Feedback in XL-MIMO Systems","abstract":"Extremely large-scale multiple-input multiple-output (XL-MIMO) systems, operating in the near-field region due to their massive antenna arrays, are key enablers of next-generation wireless communications but face significant challenges in channel state information (CSI) feedback. Deep learning has emerged as a powerful tool by learning compact channel features for feedback. However, existing methods struggle to capture the intricate structure of near-field CSI and incur prohibitive computational overhead on practical mobile devices. To overcome these limitations, we propose the near-field efficient feedback Transformer (NEFT) family for accurate near-field CSI feedback with reduced overhead under diverse hardware constraints. NEFT builds on a hierarchical vision Transformer backbone with progressive token reduction and multi-scale feature extraction, enabling compact and effective modeling of near-field channel characteristics. Furthermore, NEFT is extended with lightweight variants: NEFT-Compact applies multi-level knowledge distillation (KD) to reduce model complexity while preserving accuracy; NEFT-Hybrid adopts an attention-free CNN encoder to reduce encoder-side computation; and NEFT-Edge combines NEFT-Hybrid with KD to enable deployment on highly resource-constrained edge devices. Extensive simulations show that NEFT achieves a 15--21dB improvement in normalized mean-squared error over state-of-the-art methods, NEFT-Compact and NEFT-Edge reduce total FLOPs by 25-36% with negligible accuracy loss, while NEFT-Hybrid reduces encoder-side complexity by up to 64%, enabling deployment in highly asymmetric device scenarios. These results establish NEFT as a practical and scalable solution for near-field CSI feedback in XL-MIMO systems.","short_abstract":"Extremely large-scale multiple-input multiple-output (XL-MIMO) systems, operating in the near-field region due to their massive antenna arrays, are key enablers of next-generation wireless communications but face significant challenges in channel state information (CSI) feedback. Deep learning has emerged as a powerful...","url_abs":"https://arxiv.org/abs/2509.12748","url_pdf":"https://arxiv.org/pdf/2509.12748v3","authors":"[\"Tianqi Mao\",\"Haiyang Li\",\"Shufeng Tan\",\"Pengyu Wang\",\"Guangyao Liu\",\"Ruiqi Liu\",\"Leyi Zhang\",\"Meng Hua\",\"Dezhi Zheng\",\"Zhaocheng Wang\",\"Sheng Chen\"]","published":"2025-09-16T07:08:11Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Vision Transformer\",\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}