Reconfigurable intelligent surfaces (RISs) offer a low-cost, energy-efficient means for enhancing wireless coverage. Yet, their inherently programmable reflections may unintentionally amplify interference, particularly in large-scale, multi-RIS-enabled mobile communication scenarios where dense user mobility and frequent line-of-sight overlaps can severely degrade the signal-to-interference-plus-noise ratio (SINR). To address this challenge, this paper presents a novel generative multi-RIS control framework that jointly optimizes the ON/OFF activation patterns of multiple RISs in the smart wireless environment and the phase configurations of the activated RISs based on predictions of multi-user trajectories and interference patterns. We specially design a long short-term memory (LSTM) artificial neural network, enriched with speed and heading features, to forecast multi-user trajectories, thereby enabling reconstruction of future channel state information. To overcome the highly nonconvex nature of the multi-RIS control problem, we develop a Riemannian diffusion model on the torus to generate geometry-consistent phase-configuration, where the reverse diffusion process is dynamically guided by reinforcement learning. We then rigorously derive the optimal ON/OFF states of the metasurfaces by comparing predicted achievable rates under RIS activation and deactivation conditions. Extensive simulations demonstrate that the proposed framework achieves up to 30\% SINR improvement over learning-based control and up to 44\% gain compared with the RIS always-on scheme, while consistently outperforming state-of-the-art baselines across different transmit powers, RIS configurations, and interference densities.

翻译：可重构智能表面（RIS）为增强无线覆盖提供了一种低成本、高能效的手段。然而，其固有的可编程反射特性可能无意中放大干扰，尤其是在大规模、多RIS赋能的移动通信场景中，密集的用户移动性和频繁的视距重叠会严重恶化信干噪比（SINR）。为应对这一挑战，本文提出了一种新颖的生成式多RIS控制框架，该框架基于多用户轨迹和干扰模式的预测，联合优化智能无线环境中多个RIS的激活/关闭模式以及被激活RIS的相位配置。我们专门设计了一种融合速度与航向特征的长短期记忆（LSTM）人工神经网络，以预测多用户轨迹，从而实现对未来信道状态信息的重构。为克服多RIS控制问题的高度非凸性，我们在环面上开发了一种黎曼扩散模型，用于生成几何一致的相位配置，其中反向扩散过程由强化学习动态引导。随后，通过比较RIS激活与关闭条件下的预测可达速率，我们严格推导出超表面的最优激活/关闭状态。大量仿真结果表明，所提框架相比基于学习的控制方法可实现高达30%的SINR提升，与RIS始终激活方案相比增益高达44%，并且在不同的发射功率、RIS配置和干扰密度下均持续优于现有先进基线方法。