Distributed Deep Learning with RIS Grouping for Accurate Cascaded Channel Estimation

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.

💡 Research Summary

This paper addresses a critical bottleneck in the practical deployment of Reconfigurable Intelligent Surfaces (RIS) for 6G wireless networks: the estimation of the cascaded Base Station (BS)-RIS-user channel. Due to the passive nature of RIS elements, the individual BS-RIS and RIS-user channels cannot be measured directly. Only their cascaded product is observable, leading to a channel parameter space that scales with the product of the number of RIS elements (N) and BS antennas (M). Traditional estimation methods like Least Squares (LS) require a pilot overhead proportional to NM, which becomes prohibitive for large-scale RIS, incurring high training overhead, computational complexity, and energy consumption.

To tackle this multifaceted challenge, the authors propose a comprehensive deep learning (DL)-based framework built upon three synergistic pillars:

1. RIS Grouping: The physical dimensionality of the problem is reduced by grouping adjacent RIS elements into clusters, controlled as a single unit. This reduces the effective number of reflective control units from N to N’ (where N’ = N/g for group size g), thereby compressing the cascaded channel matrix from size N×M to N’×M. While this trades off some spatial resolution, it fundamentally lowers the minimum pilot requirement and computational load for channel estimation.

2. Distributed Machine Learning (DML) Strategy: Conventional DL models trained on data from a single user or specific scenario suffer from poor generalization to new user locations and propagation conditions (e.g., varying angle spreads, blockages). To achieve robust generalization across a heterogeneous network, the authors employ a distributed learning paradigm, specifically a synchronous Federated Averaging (FedAvg) protocol. In this strategy, the BS and multiple users collaboratively train a shared global neural network model. Users train local models on their privately collected channel datasets, and only the model parameter updates (not the raw data) are sent to the BS for secure aggregation into an improved global model. This process leverages diverse, non-IID data from across the network to build a model that performs well under a wide range of conditions.

3. Hierarchical Region-Gated Neural Architecture: Built on top of the DML framework, the authors design a specialized estimator using a Mixture of Experts (MoE) architecture with a region-based gating mechanism. A lightweight classifier (the gate) first identifies the coarse propagation region (e.g., sector 1, 2,… R) of the user. Based on this classification, only the corresponding region-specific “expert” neural network is activated to perform feature extraction and reconstruct the full channel from the limited pilot observations. This design ensures that during inference at the user equipment (UE), the computational cost is comparable to running a single compact Convolutional Neural Network (CNN), while benefiting from the specialized knowledge encoded in each expert.

The paper provides a detailed system model, formalizing the observation model with RIS grouping. It presents a complexity analysis showing reduced Multiply-Accumulate (MAC) operations for the proposed gated expert compared to a monolithic network. Extensive simulations demonstrate that the proposed framework significantly outperforms conventional LS and Minimum Mean-Square Error (MMSE) estimators, as well as single-user trained DL models, in terms of Normalized Mean-Squared Error (NMSE) under limited pilot regimes. Crucially, it maintains high accuracy across unseen user locations and propagation scenarios, validating its superior generalization capability. The results collectively demonstrate the practicality and effectiveness of this integrated approach for accurate and efficient cascaded channel estimation in future RIS-assisted 6G systems.