Who Can Safely Wait? Risk-Aware 5G Scheduling for Low-Altitude UAV Operation

2026-10-05

AI Summary

This study addresses the operational risks arising from cellular networks that overlook information latency for low-altitude unmanned aerial vehicles (UAVs) by proposing a Risk-Aware (RA) uplink scheduling framework. This framework pioneers the integration of regulatory risk proxies with Lyapunov optimization to translate information freshness into quantifiable operational risks, thereby enabling consequence-driven, Max-Weight risk-oriented priority scheduling rather than relying solely on conventional network metrics. Experimental evaluations conducted in São Paulo and Formula 1 scenarios demonstrate that the proposed approach reduces the 99th percentile risk by 86%–96%. These results indicate that the RA framework effectively supports the safe operation of larger-scale UAV fleets, offering a robust solution for integrating low-altitude aerial systems into existing cellular infrastructure while strictly adhering to regulatory safety requirements.

Abstract

Cellular-connected UAV operations already fly in many countries. However, cellular networks were designed for terrestrial users and remain blind to the operational consequences of delaying information from low-altitude aircraft. We present Risk-Aware (RA), a framework that translates information freshness and operational context into aircraft-specific operational risk, enabling uplink scheduling based on operational consequence rather than network metrics alone. RA balances the consequence of further delay against the service cost of heterogeneous single- and multi-slot updates. It combines a regulation-inspired risk proxy, risk-directed prioritization based on marginal risk growth, and a Max-Weight scheduler with Lyapunov-based performance guarantees. Although RA is derived from an analytically tractable model, we evaluate it under moving UAV trajectories, dynamic operational context, geographically varying population exposure, and realistic UAV video traffic. Across 48 operating points and fleets of up to 120 UAVs in São Paulo delivery operations and the Interlagos Formula 1 Grand Prix setting, RA reduces 99th-percentile per-UAV risk by up to 96% over proportional fair and 86% over round robin, while supporting fleets up to 1.5$\times$ and 3$\times$ larger, respectively, within the same risk envelope.

Problem

• UAV scheduling
• risk-aware
• 5G networks
• information freshness
• low-altitude operation

Innovation

• Risk-Aware Scheduling
• UAV Communication
• Information Freshness
• Lyapunov Optimization
• 5G Networks