AI 中文总结
本文综述用于无人机搭载RIS的轻量级AI技术，涵盖RL、FL等方法，分析现有工作与权衡，确定研究挑战并通过案例展示MAB方案对系统性能的影响。
AI 中文摘要
无人机（UAV）搭载的可重构智能表面（RIS）已成为增强6G网络无线覆盖、频谱效率和能量性能的有前景架构。通过将可编程电磁波操控与空中机动性相结合，UAV-RIS系统可实现动态阻塞缓解、自适应波束成形，以及在地面、海上和卫星集成环境中的灵活部署。然而，UAV轨迹、RIS相位配置与资源分配的联合优化会带来高计算复杂度，这与UAV平台严格的能量和机载处理约束不兼容。轻量级AI技术为该挑战提供了实用解决方案。因此，本文对用于UAV搭载RIS系统的轻量级AI技术进行全面综述，包括强化学习（RL）、元学习、联邦学习（FL）、多臂老虎机（MAB）及能量感知优化。我们对现有工作进行详细分类和对比分析，强调计算-能量权衡，并确定可扩展、高能效机载智能表面的开放研究挑战。此外，我们呈现一个案例研究，展示MAB方案对UAV搭载RIS中吞吐量和能量效率的影响。
英文摘要
Unmanned Aerial Vehicles (UAV)-mounted Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising architecture for enhancing wireless coverage, spectral efficiency, and energy performance in 6G networks. By combining programmable electromagnetic wave manipulation with aerial mobility, UAV-RIS systems enable dynamic blockage mitigation, adaptive beamforming, and flexible deployment across terrestrial, maritime, and satellite-integrated environments. However, joint optimization of UAV trajectory, RIS phase configuration, and resource allocation incurs high computational complexity, which is incompatible with the strict energy and onboard processing constraints of UAV platforms. Lightweight AI techniques offer practical solutions to this challenge. Hence, this paper provides a comprehensive overview of lightweight AI techniques for UAV-mounted RIS systems, including Reinforcement Learning (RL), meta-learning, Federated Learning (FL), Multi-Armed Bandits (MAB), and energy-aware optimization. We present a detailed taxonomy and comparative analysis of existing work, highlight computational-energy trade-offs, and identify open research challenges for scalable, energy-efficient airborne intelligent surfaces. Furthermore, we present a case study demonstrating the effect of MAB schemes on throughput and energy efficiency in UAV-mounted RIS.
