5G-Based autonomous ground risk mitigation for UAVs

Ouahouah, S., Harchaoui, M. L., Bekkouche, O., Bagaa, M. et Jäntti, R. (2025). 5G-Based autonomous ground risk mitigation for UAVs. Dans IEEE International Conference on Communications DOI 10.1109/ICC52391.2025.11160843.

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Résumé

As urban aviation continues to expand, ensuring the safe and efficient operation of Unmanned Aerial Vehicles (UAVs) in densely populated areas is of crucial importance. At the same time, the emergence of 5 G networks offers new potential for UAV operation in urban environments by providing Ultra-Reliable Low-Latency Communication (URLLC) and realtime analytics of population mobility. This paper proposes an enhanced framework for UAV autonomous ground risk assessment and mitigation, harnessing the Machine Learning (ML) analytics that are natively incorporated in 5G networks, and allowing the UAVs to autonomously adjust their flight path to minimize the risks to the population on the ground. Simulation results demonstrate the effectiveness of our solution in reducing ground risk, minimizing travel distance, and enhancing obstacle clearance, ultimately improving the safety of UAV operations in urban settings.

Type de document:	Document issu d'une conférence ou d'un atelier
Mots-clés libres:	5G Autonomous path planning Deep reinforcement learning Network analytics Path planning UAVs
Date de dépôt:	14 janv. 2026 16:21
Dernière modification:	14 janv. 2026 16:21
Version du document déposé:	Post-print (version corrigée et acceptée)
URI:	https://depot-e.uqtr.ca/id/eprint/12495

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