The integration of cellular communication with Unmanned Aerial Vehicles (UAVs) extends the range of command and control and payload communications of autonomous UAV applications. Accurate modeling of this air-to-ground wireless environment aids UAV mission planning. Models built on and insights obtained from real-life experiments intricately capture the variations in air-to-ground link quality with UAV position, offering more fidelity for simulations and system design than those that rely on generic theoretical models designed for ground scenarios or ray-tracing simulations. In this work, we conduct aerial flights at the Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) Lake Wheeler testbed to study the variation in key performance indicators (KPIs) of a private 4G/5G cellular base station (BS) with the UAV's altitude, distance from the BS, elevation, and azimuth relative to the BS. Variations in 4G and 5G physical layer KPIs and application layer throughput are logged and analyzed, using two Android smartphones: a Keysight Nemo device, with enhanced KPI access, through a rooted operating system, and a standard smartphone running a custom application that utilizes open-source Android APIs. The observed signal strength measurements are compared to theoretical predictions from free space path loss models that incorporate the BS antenna radiation patterns. Mathematical model parameters for polynomial curve approximations are derived to fit the observed data. Light machine learning approaches, namely random forests, gradient boosting regressors and neural networks, are used to model KPI behaviour as a function of UAV position relative to the BS. The insights and models generated from real-life experiments in this study can serve as valuable tools in the design, simulation and deployment of cellular communication-based UAV systems.

翻译：蜂窝通信与无人飞行器（UAV）的集成扩展了自主无人机应用中指令控制与有效载荷通信的覆盖范围。对空地无线环境的精确建模有助于无人机任务规划。基于真实实验构建的模型及从中获得的洞察力，能够精细刻画空地链路质量随无人机位置的变化特性，相比基于地面场景通用理论模型或射线追踪仿真的方法，为仿真与系统设计提供了更高保真度。本研究在美国先进无线实验研究平台（AERPAW）Lake Wheeler测试床开展无人机飞行实验，探究私有4G/5G蜂窝基站（BS）的关键性能指标（KPI）随无人机高度、距基站距离、相对于基站的仰角与方位角的变化规律。利用两款安卓智能手机（一款通过root操作系统增强KPI接入能力的Keysight Nemo设备，以及另一款运行基于开源安卓API定制应用程序的标准智能手机），记录并分析了4G与5G物理层KPI及应用层吞吐量的变化。观测到的信号强度测量值与融合基站天线方向图的自由空间路径损耗模型的理论预测值进行了对比。推导出多项式曲线近似的数学模型参数以拟合观测数据，并采用随机森林、梯度提升回归器与神经网络等轻量级机器学习方法，建立KPI随无人机相对基站位置变化的函数模型。本研究通过真实实验生成的洞察与模型，可为基于蜂窝通信的无人机系统的设计、仿真与部署提供重要工具。