无人机轨迹控制和功率优化,用于在联合学习环境中的低延迟C-V2X通信
Xavier Fernando1, Abhishek Gupta1
1Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B2K3, Canada.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
联合学习优化了6G车辆网络中的无人机 (UAV) 轨迹和车辆选择,降低了能源消耗,并在阴暗的色环境中提高了服务质量.
科学领域:
- 无线通信无线通信
- 6G 车载网络 6G 车载网络
- 人工智能在通信中的应用
背景情况:
- 6G车辆网络面临着视线 (LoS) 阻塞,阴影色和自动驾驶等应用程序的高数据负载等挑战.
- 无人驾驶飞行器 (UAV) 辅助的通信至关重要,但受到移动性,影像和有限的UAV资源 (电池,覆盖) 的影响.
- 数据密集型,非i.i.d和异质传感器数据增加了处理延迟,并要求无人机高效的资源管理.
研究的目的:
- 在无人机辅助的6G车载网络中调查系统性能和通信中断,考虑在正交时频空间 (OTFS) 通道中的多普勒效应.
- 为无人机轨迹预测和车辆选择提出一种低复杂度的联合学习 (FL) 方法,以提高服务质量 (QoS).
- 通过共同优化传输窗口,传输功率和UAV轨迹来最大限度地降低UAV加权总能耗.
主要方法:
- 开发了一个用于UAV轨迹预测和车辆选择的联合学习算法,利用历史轨迹数据.
- 在UAV-C-V2X通信的OTFS调制通道中,在多普勒效应下研究了系统性能.
- 联合优化传输窗口 (Lw),传输功率和无人机轨迹,以最大限度地降低加权总能耗.
主要成果:
- 基于OTFS的系统与联合学习实现了高达10%的减轻加权总能耗,通过使车辆的本地数据处理.
- 与凸优化,启发式和元启发式方法相比,提出的联合学习算法显示,加权总能耗下降了10-15%.
- 联合学习有效地利用来自过去轨迹的相关信息来改进无人机操作和资源管理.
结论:
- 联合学习提供了一个可行的解决方案,通过优化无人机轨迹和车辆选择来增强无人机辅助6G车辆网络的 QoS.
- 拟议的方法可以显著降低无人机的能源消耗,同时解决移动性,影像和数据异质性所带来的挑战.
- 这种方法可以提高未来智能运输系统中数据密集型应用程序的效率和性能.
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