在UAV-MEC网络中共同延迟和能源消耗最小化合作计划
Ming Cheng1, Saifei He1, Yijin Pan2
1School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
我们为无人机 (UAV) 辅助的移动边缘计算 (MEC) 提出了两个多代理深度增强学习方案,以优化物联网 (IoT) 中的延迟和能耗. 这两种方案都有效地管理复杂,动态的环境.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 电信工程 电信工程 电信工程
背景情况:
- 物联网 (IoT) 应用要求大规模的设备协作,繁重的计算和低延迟.
- 无人机 (UAV) 辅助的移动边缘计算 (MEC) 为用户设备 (UD) 提供灵活,广泛的覆盖服务.
- 在动态的大型网络中,优化延迟和能源消耗之间的权衡是一个重大挑战.
研究的目的:
- 在无人机辅助MEC系统中共同优化关联,卸载和计算资源分配.
- 解决延迟和能源消耗之间的固有权衡,以提高系统性能.
- 开发能够处理复杂和动态网络环境的智能系统.
主要方法:
- 制定了作为部分可观察的马尔科夫决策过程 (POMDP) 的联合优化问题.
- 开发了两个多代理深度强化学习 (DRL) 方案:多代理近距离政策优化 (MAPPO) 和封闭式增强多武装强盗 (CF-MAB).
- UDs作为独立的代理人,从互动和历史数据中学习,以最大限度地提高个人奖励,促进隐含的协作.
主要成果:
- 两项拟议的DRL方案都在平衡延迟和能源消耗方面表现出有效性.
- 在复杂,动态的环境中,MAPPO计划在协作决策方面表现出色,以实现高性能.
- 该CF-MAB计划通过将关联与卸载和资源分配分离,提供快速,独立的响应决策.
结论:
- 拟议的多代理DRL方法成功地解决了在无人机辅助MEC系统中联合优化的挑战.
- 对于不同的运营需求,MAPPO和CF-MAB提供了明显的优势,提高了系统性能和效率.
- 这些智能方案为未来的大规模物联网应用提供了强大的解决方案,需要高效的资源管理.
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