Problem
Research questions and friction points this paper is trying to address.
air-to-ground communication
UAV
cellular KPIs
5G testbed
channel modeling
Modeling and Analysis of Air-to-Ground Cellular KPIs in a 5G Testbed using Android Smartphones
🤖 AI Summary
This work addresses the lack of high-fidelity, experimentally validated models for aerial–terrestrial cellular communication links, which hinders unmanned aerial vehicle (UAV) system design and mission planning. Leveraging the AERPAW Lake Wheel testbed, the study employs custom Android devices to collect enhanced key performance indicators (KPIs) over real 4G/5G networks, integrating spatial parameters such as flight altitude, slant range, elevation angle, and azimuth. By fusing free-space path loss theory with empirical measurements, the authors develop a joint physical- and application-layer empirical channel model. Lightweight machine learning techniques—including random forests, gradient boosting, and neural networks—are incorporated to predict spatial variations in KPIs. The resulting model accurately captures the characteristics of aerial–terrestrial links, offering a practical tool for simulation and system design of cellular-connected UAVs.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
air-to-ground cellular
UAV KPI modeling
real-world 5G testbed
light machine learning
Android-based measurement
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