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：摘要：蜂窝通信与无人驾驶飞行器（UAV）的集成拓展了自主无人机应用中指挥控制与有效载荷通信的范围。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，能够精细捕捉无人机位置变化对空地链路质量的影响，相比用于地面场景的通用理论模型或射线追踪模拟，为仿真与系统设计提供了更高的保真度。本研究在美国先进无线空中实验与研究平台（AERPAW）的Lake Wheeler测试床开展飞行实验，探究私有4G/5G蜂窝基站（BS）的关键性能指标（KPI）随无人机相对于BS的高度、距离、仰角及方位角的变化规律。通过两部安卓智能手机（一部为具备增强KPI访问能力的Keysight Nemo设备，其操作系统已root；另一部为运行自定义开源安卓API应用程序的普通智能手机），记录并分析了4G与5G物理层KPI及应用层吞吐量的变化。将观测到的信号强度测量值与基于基站天线辐射模式的自有空间路径损耗模型的理论预测进行对比，并推导了多项式曲线近似的数学模型参数以拟合观测数据。采用轻量级机器学习方法（即随机森林、梯度提升回归器及神经网络），将KPI行为建模为无人机相对于BS位置的函数。本研究中基于真实实验获得的洞察与模型，可作为基于蜂窝通信的无人机系统设计、仿真及部署中的有效工具。