深度强化学习用于联合轨迹规划,传输调度和无人机辅助无线传感器网络的访问控制
Xiaoling Luo1,2, Che Chen3,4, Chunnian Zeng1
1School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
优化无人飞行器 (UAV) 路径和访问控制可以提高无线传感器网络的能源效率. 多代理深度强化学习提高了数据收集和传输性能.
科学领域:
- 无线传感器网络 无线传感器网络
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 无人机可以通过将传感信息和计算任务转发到远程基站 (RBS) 来支持地面用户.
- 优化无人机轨迹,调度和访问控制对于在地面无线传感器网络中节能收集和传输数据至关重要.
- 由于GU分布和流量需求的动态性,无人机辅助网络面临着挑战.
研究的目的:
- 提高无人机辅助无线传感器网络中传感数据收集和传输的能源效率.
- 调查无人机访问控制和轨迹规划之间的权衡,在一个时间区分的框架结构中.
- 为动态网络环境开发一个高效的学习框架.
主要方法:
- 采用多代理深度强化学习方法来优化无人机轨迹,调度和访问控制策略.
- 设计了一个层次化的学习框架,以减少动作和状态空间,提高学习效率.
- 这项研究考虑了一种有时间槽的框架结构,其中包含了飞行,传感和信息转发的子槽.
主要成果:
- 无人机轨迹规划与通道控制相结合,可以显著提高无人机的能源效率.
- 层次学习方法表现出增强的学习稳定性和卓越的传感性能.
- 模拟结果验证了在动态网络条件下提议的优化策略的有效性.
结论:
- 优化无人机轨迹和访问控制是提高无线传感器网络能源效率的关键.
- 层次化的多代理深度增强学习为无人机辅助传感网络提供了稳定有效的解决方案.
- 拟议的框架有效地应对动态环境和不确定的用户需求所带来的挑战.
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