Resilient UAV Routes for Emercency Services Using 5G Networks

This study proposes an advanced system for managing urban air transport during emergency scenarios using 5G networks as an alternative positioning system in dense urban environments, where the GNSS signals are often affected. The system is evaluated within a realistic scenario, in a predefined clustering of the existing cellular network in Valencia (Spain), assuming all stations are equipped with 5G technology.
It dynamically generates resilient routes connecting a strategic point (A) with an emergency random point (B), prioritizing accuracy across congested urban areas. The core of the route planning algorithm relies on an accuracy metric, the Cramér Rao Lower Bound (CRLB), and incorporates dynamic base station selection and multilateration techniques to improve robustness. Furthermore, the system implicitly integrates intercluster soft handover procedures, ensuring continuity and precision as
the UAV transitions across network regions. The simulated results use the Positioning Reference Signals (PRS) from 5G, comparing the Root Mean Square Error (RMSE) and route performance between direct and three optimized paths, using trajectories with a fixed-point A and a random point B. The results demonstrate the system’s ability to improve the precision and generate adaptive air routes for emergency support in complex urban environments without GNSS systems.