AI 中文总结
研究无人机搭载RIS的实时性能，通过扩展卡尔曼滤波器和机载传感器预测RIS姿态，实现实时重新配置，减轻干扰影响，保持性能增益，并评估不同部署位置对RIS性能的影响。
AI 中文摘要
可重构智能表面（RIS）因其能够动态控制传播环境而成为第六代（6G）无线网络的关键技术。为确保在实际应用中有良好的视距（LoS）条件，RIS安装在无人机（UAV）上。虽然无人机搭载RIS的潜力在理论研究中已得到广泛探讨，但利用实际数据进行的实验验证仍然有限。本文展示了首个功能齐全、具备实时能力的无人机搭载RIS原型，并在实际干扰和硬件约束下通过实验测量验证其性能。我们表明可基于无人机的扩展卡尔曼滤波器（EKF）和机载传感器预测RIS姿态。利用此估计，我们证明RIS可实时重新配置，有效减轻干扰影响并保持RIS链路的性能增益。此外，我们系统评估了不同部署位置，以深入了解实际场景中RIS的性能。
英文摘要
Reconfigurable intelligent surfaces (RIS) are emerging as a key technology for sixth-generation (6G) wireless networks due to their ability to dynamically control the propagation environment. To ensure favorable Line-of-Sight (LoS) conditions in real-world applications, the RIS is mounted on an unmanned aerial vehicle (UAV). While the potential of UAV-mounted RIS has been extensively studied in theoretical works, experimental validation with real-world data remains limited. Such validation is particularly important, as UAV motion and disturbances may degrade the performance of the RIS-enabled link. In this paper, we present the first fully functional, real-time capable UAV-mounted RIS prototype and validate its performance through experimental measurements under realistic disturbances and hardware constraints. We show that the RIS pose can be predicted based on the UAV's extended Kalman filter (EKF) and onboard sensors. By utilizing this estimation, we demonstrate that the RIS can be reconfigured in real time, effectively mitigating disturbance effects and preserving the performance gains of the RIS-enabled link. Furthermore, we systematically evaluate different deployment locations to provide insights into RIS performance in real-world scenarios.
Comments10 pages, 6 figures, Submitted to IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
