基于Q学习的无人机安装基站定位在灾难场景中连接到位于未知位置的用户
1Department of ECE, National Institute of Technology Patna, Patna, Bihar 800005 India.
The Journal of supercomputing
|June 26, 2023
概括
这项研究引入了一种新方法,用于部署无人驾驶飞行器安装基站 (UmBS),以恢复灾难后的无线服务. 这种方法改善了用户本地化,并优化了基站功率,以获得更好的数据速率和更少的中断.
科学领域:
- 无线通信网络是无线通信网络.
- 灾难恢复技术 灾难恢复技术
- 移动计算 移动计算
背景情况:
- 无人驾驶飞行器安装基站 (UmBS) 为灾区恢复无线服务提供了灵活的解决方案.
- 关键的挑战包括准确的地面用户设备 (UE) 定位,UmBS传输功率优化和UE-UmBS关联.
- 现有的方法通常假定已知的UE位置,限制它们在现实世界的灾难场景中的适用性.
研究的目的:
- 提出一种新的方法,即本地化地面UEs及其与UmBS (LUAU) 的关联,以实现有效的UmBS部署.
- 解决地面UE本地化和节能UmBS部署的关键问题.
- 在受灾地区加强无线服务恢复.
主要方法:
- 引入了一种基于三维范围的定位方法 (3D-RBL),可以在没有先前知识的情况下估计地面UE位置.
- 一个优化问题是通过优化UmBS传输功率和部署位置来最大限度地提高平均数据速率,同时考虑UmBS之间的干扰.
- 使用Q学习框架来解决优化问题,利用其探索和开发能力.
主要成果:
- 与两个基准方案相比,拟议的LUAU方法显示出更高的性能.
- 在地面用户单位的平均数据速率中观察到显著的改善.
- 在中断百分比上实现了显著的降低,这表明服务可靠性得到了提高.
结论:
- LUAU方法有效地解决了UE本地化和UmBS在灾难场景中部署的挑战.
- 优化UmBS传输功率和部署位置,以及准确的UE本地化,可以提高网络性能.
- 这种方法为使用UmBS快速有效地恢复无线服务提供了一个有希望的解决方案.
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