在无人机通信网络中通过基于奖励的多代理学习进行分散的资源配置
Muhammad Shoaib1, Ghassan Husnain2, Muhsin Khan3
1Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, 25100, Pakistan.
Scientific reports
|September 26, 2025
概括
无人驾驶飞行器 (UAV) 通过独立管理用户和电力等资源来优化无线访问. 一个新的基于奖励的多代理学习 (RMAL) 框架最大限度地提高了系统奖励,而无需完全交换信息.
科学领域:
- 无线通信系统无线通信系统
- 人工智能在网络中的作用
- 资源管理 资源管理
背景情况:
- 无人驾驶飞行器 (UAV) 提供灵活的空中基站 (ABS) 可按需无线访问.
- 动态资源配置对于最大限度地提高多无人机系统的性能至关重要.
- 现有的方法可能需要广泛的信息交换,增加系统开销.
研究的目的:
- 研究多个无人机支持的通信系统的动态资源分配策略.
- 为了最大限度地提高长期回报和整体系统性能.
- 为无人机资源管理开发一个分散的学习框架.
主要方法:
- 将资源分配问题建模为一个随机游戏,无人机作为学习代理.
- 开发一个基于奖励的多代理学习 (RMAL) 框架.
- 采用基于Q学习的框架与本地观察实施一个独立于代理者的策略.
主要成果:
- 拟议的RMAL框架实现了有效的资源分配,而不需要无人机之间全面的信息交换.
- 模拟结果表明,RMAL性能对参数调整敏感,但提供了良好的权衡.
- 与完全信息共享的系统相比,该方法提供了可接受的性能.
结论:
- RMAL框架为多无人机系统中的动态资源分配提供了一种有效的方法.
- 使用本地观察的分散学习可以实现近乎最佳的性能,同时减少通信开销.
- 这项研究有助于在空中通信网络中推进智能资源管理.
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