Exploiting Core Openness as Native-AI Enabler for Optimised UAV Flight Path Selection

Given that the use of the unmanned aerial vehicles (UAVs) has been increased significantly the last years, it is deemed necessary to adopt innovative technologies and features, with a focus on automating drones’ missions so as to revolutionize the way vertical industries operate. This automation can be achieved by the integration of UAVs in the cellular networks however, operational challenges such as signal quality variation of the network need to be addressed. In this regard, the paper discusses the potential for optimizing the automated UAV flights over the cellular networks. The optimization is focusing on an approach based on 3GPP Application Programming Interfaces (APIs) and the openness of the core network for predicting the Quality of Service (QoS). Through the prediction of the QoS, the optimization of the flight path for UAVs in 5G and B5G networks is proposed and validated on top of an emulation tool that can support scenarios with UAVs.