用强化学习动态分配无人机资源，兼顾安全、低延迟与省电。
RL-Driven Security-Aware Resource Allocation Framework for UAV-Assisted O-RAN
- 基于强化学习实时优化无人机资源分配
- 在搜救场景中实现低延迟、高安全、节能三重提升
- 适合应急通信、无人机网络等动态环境应用
将无人机集成到开放无线接入网（O-RAN）可提升灾难救援和搜救任务中的通信能力，确保基础设施失效时仍能保持连接。但搜救场景对安全性和低延迟要求极高，任何延迟或信息泄露都可能影响任务成败。尽管无人机可作为移动中继，却带来能耗与资源管理挑战，需智能分配策略。现有方法常忽视安全、时延与能效的联合优化。本文提出一种基于强化学习（RL）的动态资源分配框架，显式处理三者权衡。该方法建模了安全感知的资源分配、时延最小化与能效优化问题，并通过RL求解。相比启发式或静态方法，本框架能实时适应网络动态，保障通信鲁棒性。仿真显示，相较于启发式基线，本方案在搜救场景中实现了更高安全性与能效，同时维持超低时延。
原文摘要 · Abstract (English)
The integration of Unmanned Aerial Vehicles (UAVs) into Open Radio Access Networks (O-RAN) enhances communication in disaster management and Search and Rescue (SAR) operations by ensuring connectivity when infrastructure fails. However, SAR scenarios demand stringent security and low-latency communication, as delays or breaches can compromise mission success. While UAVs serve as mobile relays, they introduce challenges in energy consumption and resource management, necessitating intelligent allocation strategies. Existing UAV-assisted O-RAN approaches often overlook the joint optimization of security, latency, and energy efficiency in dynamic environments. This paper proposes a novel Reinforcement Learning (RL)-based framework for dynamic resource allocation in UAV relays, explicitly addressing these trade-offs. Our approach formulates an optimization problem that integrates security-aware resource allocation, latency minimization, and energy efficiency, which is solved using RL. Unlike heuristic or static methods, our framework adapts in real-time to network dynamics, ensuring robust communication. Simulations demonstrate superior performance compared to heuristic baselines, achieving enhanced security and energy efficiency while maintaining ultra-low latency in SAR scenarios.
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