Deep Neural Quality of Service Prediction for Unmanned Aircraft System Communications

Commercial Unmanned Aircraft Systems (UAS) have a wide range of applications, including package delivery, inspection and search and rescue missions. For the operation of Unmanned Aircraft Vehicles (UAV) Beyond Visual Line of Sight (BVLOS), reliable long-range communication is essential. The cellular network is one possible solution, but there are issues such as signal loss and frequent handovers at higher altitudes. To mitigate these issues, our work proposes the use of two cellular links from different providers prioritised according to Quality of Service (QoS) prediction. We evaluate multiple AI-based model architectures for the prediction, and find that the model consisting of Gated Recurrent Units (GRU) and convolutional layers outperforms the others. The models are trained and tested on real-world data and show a reduction in latency peaks, thereby increasing connection resilience. Moreover, the prediction pipeline is designed to be executable on the UAV side and is not limited to a specific geographical area, making it applicable to real-world scenarios. Finally, we present a pre-flight path planning algorithm that takes QoS into account when calculating the flight path in order to further improve communication. To support the research community, we publicly share the dataset used to obtain our results.