AI 中文总结
针对RIS辅助的OTA-FL系统受时变信道等问题影响，提出统一框架，集成GRU等技术，设计动态感知分组策略，在低导频和高移动性下提升CSI估计精度等性能，增强强用户异构性下的收敛速度和鲁棒性。
AI 中文摘要
可重构智能表面（RIS）辅助的空中联邦学习（OTA-FL）实现了高效的分布式智能，但受时变信道、不完美信道状态信息（CSI）和强用户异构性影响，降低了聚合精度并导致严重的模型更新取消。为解决这些问题，我们提出了一个统一框架，用于RIS辅助的OTA-FL系统中的联合信道估计和动态感知用户分组，在不完美CSI和异构动态下实现可靠学习。该框架集成门控循环单元（GRU）进行时间建模以捕获时变CSI演变，基于OTA的联邦聚合与个性化，以及闭环中的RIS感知物理层优化。此外，我们设计了一种基于长期路径损耗和短期信道动态的动态感知分组策略，以减少异构条件下的用户间冲突。仿真结果表明，该方法在低导频和高移动性情况下，在CSI估计精度和OTA聚合性能方面取得了显著提升，同时在强用户异构性下提高了收敛速度和鲁棒性。
英文摘要
Reconfigurable intelligent surface (RIS)-assisted over-the-air federated learning (OTA-FL) enables efficient distributed intelligence but suffers from time-varying channels, imperfect channel state information (CSI), and strong user heterogeneity, which jointly degrade aggregation accuracy and cause severe model update cancellation. To address these issues, we propose a unified framework for joint channel estimation and dynamics-aware user grouping in RIS-assisted OTA-FL systems, enabling reliable learning under imperfect CSI and heterogeneous dynamics. The framework integrates gated recurrent unit (GRU) for temporal modeling to capture time-varying CSI evolution, OTA-based federated aggregation with personalization, and RIS-aware physical-layer optimization in a closed loop. In addition, we design a dynamics-aware grouping strategy based on long-term path-loss and short-term channel dynamics to reduce inter-user conflicts under heterogeneous conditions. Simulation results show that the proposed method achieves substantial gains in CSI estimation accuracy and OTA aggregation performance in low-pilot and high-mobility regimes, while improving convergence speed and robustness under strong user heterogeneity.
Comments13 pages, 12 figures. Submitted to IEEE TWC
