使用物理信息的高斯过程进行信息性路径规划,用于5G网络的空中映射
Jonas F Gruner1, Jan Graßhoff1,2, Carlos Castelar Wembers1
1Institute of Electrical Engineering in Medicine, Universität zu Lübeck, 23562 Lübeck, Germany.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
无人驾驶飞行器 (UAV) 和高斯过程 (GPs) 有效地绘制了5G私人网络. 这种方法可以将飞行距离的映射缩短98%,同时确保高精度可靠的工业覆盖.
科学领域:
- 电信工程 电信工程 电信工程
- 机器人和控制系统 机器人和控制系统
- 统计机器学习的统计学
背景情况:
- 5G私有蜂网络在工业环境中越来越多地使用.
- 确保可靠的覆盖和边界要求至关重要,但测量密集.
- 无人驾驶飞行器 (UAV) 为高效的网络映射提供了一个潜在的解决方案.
研究的目的:
- 提出一种使用无人机和高斯过程 (GPs) 的方法,以高效地绘制5G私有网络覆盖范围.
- 通过物理信息的平均函数和射线追踪模拟来提高GP预测的准确性.
- 开发一个信息化的路径规划算法,以优化无人机网络测量.
主要方法:
- 在GP模型中整合基于物理学的平均函数 (包括射线追踪).
- 使用现实世界的5GRSRP测量对不同GP平均函数的定量评估.
- 开发和硬件在循环模拟的信息路径规划算法,结合GP和贝叶斯优化.
主要成果:
- 适当的平均函数显著提高了GP预测的准确性.
- 信息化路径规划算法可将无人机飞行距离缩短高达98%.
- 与测量试验相比,保持了不到6dBm的平均平方根平均误差.
结论:
- 无人机与具有物理知识的全科医生相结合,为5G私有网络映射提供了准确有效的解决方案.
- 建议的信息化路径规划大大优化了数据收集工作.
- 这种方法对于可靠的工业5G部署和覆盖范围验证至关重要.
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