深度强化学习框架,用于共同优化多RAT无人机在异质网络中的位置和用户协会
Mohamed G Anany1, Mahmoud M Elmesalawy2, Ahmed M Abd El-Haleem2
1Department of Communications and Electronics, Canadian International College, CIC, Cairo, 11865, Egypt. mohamed_gamaleldin@cic-cairo.com.
Scientific reports
|November 7, 2025
概括
本研究引入了使用无人机 (UAV) 进行6G通信的新框架,以提高用户满意度和网络效率. 该系统优化了无人机的放置和用户关联,显著改善了关键性能指标.
科学领域:
- 无线通信无线通信
- 网络优化 网络优化
- 人工智能在电信中的应用
背景情况:
- 多媒体和物联网 (IoT) 设备的普及推动了对6G网络更高数据速率和更低功耗的需求.
- 现有的网络基础设施在满足这些不断增长的需求方面面临挑战,特别是在提供按需容量和覆盖方面.
- 无人驾驶飞行器 (UAV) 提供了一种灵活的解决方案,可以通过移动基站来增强地面网络.
研究的目的:
- 开发和评估一种代框架,以最大限度地提高无人机辅助多无线电接入技术 (Multi-RAT) 异质网络 (HetNet) 中的满意度与能源比率 (SER).
- 优化无人机的3D位置和地面用户的协会,以提高网络性能和能源效率.
- 调查深度强化学习 (DRL) 的有效性,并对优化无人机辅助无线网络的遗憾学习.
主要方法:
- 引入了一种新的满意度与能源比率 (SER) 度量,平衡用户对无人机能耗的满意度.
- 提出了一个代优化框架,集成一个修改的K-means算法进行初始化.
- 深度强化学习 (DRL) 用于优化无人机3D位置,而遗憾学习则用于用户关联.
- 进行了广泛的模拟,以根据各种指标评估框架的性能.
主要成果:
- 观察到显著的改善,包括满意度指数上升13%,下链数据率上升25%.
- 升级连接的电力消耗减少了67%,故障概率降低了71%.
- 贾恩的公平指数提高了28%,框架代减少了45%.
结论:
- 拟议的代框架有效地最大化了无人机辅助多RAT HetNets中的满意度与能量比 (SER).
- 整合DRL和遗憾学习为优化无人机3D放置和用户关联提供了强大的解决方案.
- 该框架显示了网络性能,能源效率和用户满意度的实质性改进,为先进的6G通信系统铺平了道路.
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