发表机构
Aeronautics Institute of Technology; Northwestern University（巴西航空技术学院; 西北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对蜂窝网络对低空无人机信息延迟不敏感的问题，提出风险感知调度框架RA，基于运行后果调度上行链路，在圣保罗和F1场景中显著降低无人机风险并支持更大机队。
AI 中文摘要
蜂窝连接的无人机运行已在许多国家开展。然而，蜂窝网络是为地面用户设计的，对延迟低空飞行器信息所带来的运行后果并不敏感。我们提出了风险感知（RA）框架，该框架将信息新鲜度和运行情境转化为特定于飞行器的运行风险，从而使得上行链路调度基于运行后果而非仅基于网络指标。RA在进一步延迟的后果与异构单时隙和多时隙更新的服务成本之间进行权衡。它结合了受法规启发的风险代理、基于边际风险增长的风险导向优先级排序，以及具有基于李雅普诺夫性能保证的最大权重调度器。尽管RA源自一个可解析处理的模型，我们在移动无人机轨迹、动态运行情境、地理上变化的人口暴露以及现实的无人机视频流量下对其进行了评估。在圣保罗配送运营和因特拉格斯一级方程式大奖赛场景中，跨越48个运行点和多达120架无人机的机队，RA将每架无人机的第99百分位风险相比比例公平降低了高达96%，相比轮询调度降低了86%，同时在相同的风险包络内支持分别大至1.5倍和3倍的机队规模。
英文摘要
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.
