Predictive QoS for Cellular-Connected UAV Communications

Abstract

Unmanned aerial vehicles (UAVs), or drones, are transforming industries due to their affordability, ease of use, and adaptability. This emphasizes the need for reliable communication links, especially in beyond-line-of-sight scenarios. This paper investigates the feasibility of predicting future quality of service (QoS) in UAV payload communication links, with a special focus on 5G cellular technology. Through field tests conducted in a suburban environment, we explore challenges and trade-offs that cellular-connected UAVs face, particularly in the context of frequency band selection. We employed machine learning models to forecast uplink (UL) throughput for UAV payload communication, highlighting the significance of diverse training data for accurate predictions. The results reveal the effect of frequency band selection on UAV UL throughput rates at varying altitudes and the influence of integrating diverse feature sets, including radio, network, and spatial features, on ML model performance. These insights provide a foundation for addressing the complexities in UAV communications and enhancing UAV operations in modern networks.