在STAR-RIS无人机上的服务增强机制基于深度增强学习
Junjie Yan1,2, Yichen Xu1,2, Haohao Yuan1,2
1School of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究引入了一种用于无人机和可重新配置的智能表面 (RIS) 的新方法,以改善通信. 它优化无人机角度和RIS设置,为所有用户提升服务,减少整体系统时间.
科学领域:
- 无线通信无线通信
- 空中网络 空中网络
- 超材料是指一种超材料.
背景情况:
- 无人驾驶飞行器 (UAV) 和可重新配置的智能表面 (RIS) 是提高无线通信覆盖率的关键.
- 当前的研究往往忽视了无人机悬空角度对STAR-RIS信号反射和传输的影响.
研究的目的:
- 提出一种新的STAR-RIS辅助无人机机制,用于基于实时用户分布的动态服务区分区.
- 通过共同优化无人机轨迹,悬空角度和STAR-RIS参数来提高边缘和封闭用户的频道质量.
主要方法:
- 复杂的优化问题的分解成子问题.
- 链式林-克尼根 (CLK) 算法用于无人机飞行轨迹优化.
- TD3算法用于优化STAR-RIS参数和无人机悬空角度.
主要成果:
- 对边缘和封闭用户的通道质量有显著的改善.
- 有效地减少了系统服务时间和用户传输时间.
- 在绩效指标方面表现出优于传统方法的优势.
结论:
- 拟议的机制成功地解决了在动态分区的服务区域中共同通道干扰的问题.
- 在先进的无线系统中,对无人机轨迹,悬空角度和STAR-RIS参数的联合优化至关重要.
- 这种方法为UAV-RIS集成通信系统提供了显著的进步.
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