Distributed Deep Learning with RIS Grouping for Accurate Cascaded Channel Estimation
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909959039483904 |
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| author | Rahman, Saifur Shah, Syed Luqman Khan, Salman Khan, Jalal Irfan, Muhammad Shafi, Maaz Muhammad, Said Muhammad, Fazal Akond, Mohammad Shahed |
| author_facet | Rahman, Saifur Shah, Syed Luqman Khan, Salman Khan, Jalal Irfan, Muhammad Shafi, Maaz Muhammad, Said Muhammad, Fazal Akond, Mohammad Shahed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14062 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Deep Learning with RIS Grouping for Accurate Cascaded Channel Estimation Rahman, Saifur Shah, Syed Luqman Khan, Salman Khan, Jalal Irfan, Muhammad Shafi, Maaz Muhammad, Said Muhammad, Fazal Akond, Mohammad Shahed Signal Processing 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. |
| title | Distributed Deep Learning with RIS Grouping for Accurate Cascaded Channel Estimation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.14062 |