在3D室外空气空气无线通道中通过机器学习进行方向估计
Muhammad Hashir Syed1, Maninderpal Singh1, Joseph Camp1
1Lyle School of Engineering, Southern Methodist University, Dallas, TX 75275, USA.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了用于无人机通信的机器学习方法,使用通道估计准确估计发射器方向. 这提高了天线网络的效率,通过使定向光束选择无需耗时的扫描.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 航空航天工程 航空航天工程
背景情况:
- 无人驾驶飞行器 (UAV) 需要强大的3D通信,但无线传输功耗限制范围.
- 传统的多输入多输出 (MIMO) 系统在无人机上需要大量的动力资源.
- 阶段阵列提供方向性,但涉及复杂的光束扫描和搜索延迟,特别是在动态无人机移动性方面.
研究的目的:
- 开发一种基于机器学习的高效方法,用于估计空对空 (A2A) 链路中传输节点的方向.
- 通过启用直接定向光束选择,减少无人机通信的搜索时间和复杂性.
- 用现实世界的无人机对无人机测量来验证拟议的方法.
主要方法:
- 在A2A链接中分析两个无人机之间的多天线通道.
- 开发一种机器学习模型,利用2x2 MIMO系统 (4个天线) 的通道估计.
- 通过无人机对无人机测量进行现场验证.
主要成果:
- 拟议的机器学习方法准确地估计了A2A链接中的发射器方向,准确度为86%.
- 该方法有效地减少了无人机通信中光束扫描的需要.
- 对于基于无人机的大规模MIMO系统的展示了部署能力.
结论:
- 开发的机器学习方法显著提高了无人机的定向通信效率.
- 这种方法为优化动态空中网络中的无线链路提供了实用解决方案.
- 准确的方向估计为提高无人机通信性能而没有搜索延迟铺平了道路.
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