基于无人机联合内容采集和轨迹设计的长期能源消耗最小化
Elhadj Moustapha Diallo1, Rong Chai1, Abuzar B M Adam2
1School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
使用无人机 (UAV) 缓存内容可以提高性能. 这项研究优化了无人机轨迹,功率和内容放置,使用层次深度强化学习来降低能源消耗.
科学领域:
- 无线通信网络 无线通信网络
- 优化理论 优化理论
- 人工智能的人工智能
背景情况:
- 无人驾驶飞行器 (UAV) 提供了在无线网络中改进内容传输的潜力.
- 高效的内容获取和能源管理是多无人机系统的关键挑战.
- 现有的方法与联合优化问题的复杂性作斗争.
研究的目的:
- 通过共同优化无人机轨迹,功率分配,内容获取和内容放置来最大限度地降低多个无人机网络的能源消耗.
- 解决复杂的混合整数非线性编程 (MINLP) 问题的挑战.
- 开发一种新的,高效的解决方案,用于在无人机辅助内容交付中进行随机优化.
主要方法:
- 为联合设计参数制定了一个受约束的优化问题.
- 将问题转化为半马尔科夫决策过程 (SMDP).
- 开发了一个基于选项的层次深度强化学习 (OHDRL) 技术,定义低级别的动作 (轨迹,功率) 和高级别的选项 (内容放置,获取).
主要成果:
- 提出的OHDRL方法有效地解决了联合优化问题.
- 与现有技术相比,数值结果显示了更一致的学习表现.
- 该方法实现了系统能耗的显著降低.
结论:
- OHDRL为优化复杂的无人机辅助内容传递系统提供了有效的框架.
- 开发的方法平衡了性能增长与能源效率.
- 这项研究为未来无人机网络设计和资源管理提供了有希望的方向.
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