{"ID":2869908,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.14062","arxiv_id":"2509.14062","title":"Distributed Deep Learning with RIS Grouping for Accurate Cascaded Channel Estimation","abstract":"Reconfigurable Intelligent Surface (RIS) panels are envisioned as a key technology for sixth-generation (6G) wireless networks, providing a cost-effective means to enhance coverage and spectral efficiency. A critical challenge is the estimation of the cascaded base station (BS)-RIS-user channel, since the passive nature of RIS elements prevents direct channel acquisition, incurring prohibitive pilot overhead, computational complexity, and energy consumption. To address this, we propose a deep learning (DL)-based channel estimation framework that reduces pilot overhead by grouping RIS elements and reconstructing the cascaded channel from partial pilot observations. Furthermore, conventional DL models trained under single-user settings suffer from poor generalization across new user locations and propagation scenarios. We develop a distributed machine learning (DML) strategy in which the BS and users collaboratively train a shared neural network using diverse channel datasets collected across the network, thereby achieving robust generalization. Building on this foundation, we design a hierarchical DML neural architecture that first classifies propagation conditions and then employs scenario-specific feature extraction to further improve estimation accuracy. Simulation results confirm that the proposed framework substantially reduces pilot overhead and complexity while outperforming conventional methods and single-user models in channel estimation accuracy. These results demonstrate the practicality and effectiveness of the proposed approach for 6G RIS-assisted systems.","short_abstract":"Reconfigurable Intelligent Surface (RIS) panels are envisioned as a key technology for sixth-generation (6G) wireless networks, providing a cost-effective means to enhance coverage and spectral efficiency. A critical challenge is the estimation of the cascaded base station (BS)-RIS-user channel, since the passive natur...","url_abs":"https://arxiv.org/abs/2509.14062","url_pdf":"https://arxiv.org/pdf/2509.14062v2","authors":"[\"Saifur Rahman\",\"Syed Luqman Shah\",\"Salman Khan\",\"Jalal Khan\",\"Muhammad Irfan\",\"Maaz Shafi\",\"Said Muhammad\",\"Fazal Muhammad\",\"Mohammad Shahed Akond\"]","published":"2025-09-17T15:05:32Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}