Yan Junjie, Xu Yichen, Yuan Haohao, Xue Chunhua
School of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China.
Guangxi Key Laboratory of Multidimensional Information Fusion for Intelligent Vehicles, Liuzhou 545006, China.
Sensors (Basel). 2025 Mar 20;25(6):1943. doi: 10.3390/s25061943.
UAVs and reconfigurable intelligent surfaces (RISs) have emerged as promising solutions to enhance communication coverage and performance. However, existing studies primarily focus on optimizing the amplitude and phase shift of a STAR-RIS without considering the impact of varying UAV hovering angles on signal reflection and transmission. In this paper, we propose a novel STAR-RIS-assisted UAV service enhancement mechanism that dynamically adjusts reflection/transmission regions based on the real-time user distribution, significantly improving the channel quality for both edge and occluded users. This work is the first to jointly optimize the phase and amplitude of the STAR-RIS, the UAV flight trajectory, and the hovering angle, addressing the critical challenge of co-channel interference caused by dynamically partitioned service areas. The complex optimization problem is decomposed into subproblems, where the UAV flight trajectory is optimized using the Chained Lin-Kernighan (CLK) algorithm and the STAR-RIS parameters and UAV hovering angle are optimized using the TD3 algorithm. The experimental results show that the proposed mechanism effectively reduces the system service time and user transmission time, outperforming traditional methods.
无人机和可重构智能表面（RIS）已成为增强通信覆盖范围和性能的有前景的解决方案。然而，现有研究主要集中在优化智能反射阵列RIS的幅度和相移，而未考虑无人机悬停角度变化对信号反射和传输的影响。在本文中，我们提出了一种新颖的智能反射阵列RIS辅助无人机服务增强机制，该机制基于实时用户分布动态调整反射/传输区域，显著提高了边缘用户和被遮挡用户的信道质量。这项工作首次联合优化了智能反射阵列RIS的相位和幅度、无人机飞行轨迹以及悬停角度，解决了动态划分服务区域导致的同信道干扰这一关键挑战。复杂的优化问题被分解为子问题，其中无人机飞行轨迹使用链式林-克努汉（CLK）算法进行优化，智能反射阵列RIS参数和无人机悬停角度使用TD3算法进行优化。实验结果表明，所提出的机制有效减少了系统服务时间和用户传输时间，优于传统方法。
