RL-Driven Security-Aware Resource Allocation Framework for UAV-Assisted O-RAN

2025-10-20

AI Summary

In drone-assisted Open Radio Access Network (O-RAN) for disaster search-and-rescue (SAR), jointly optimizing security, ultra-low latency (<10 ms), and energy efficiency remains challenging due to dynamic emergency conditions. Method: This paper proposes the first reinforcement learning–driven resource scheduling framework tailored to dynamic SAR scenarios. It formulates security awareness as a hard constraint and jointly optimizes UAV-relay power allocation, link selection, and computation offloading under the O-RAN RAN Intelligent Controller (RIC). Contribution/Results: Simulation results demonstrate that, under end-to-end latency <10 ms, the framework improves secure key generation success rate by 37% and reduces per-task energy consumption by 29%, outperforming conventional heuristic and static approaches. To our knowledge, this is the first work to systematically achieve three-dimensional co-optimization of security, latency, and energy efficiency within the O-RAN architecture, delivering a deployable intelligent scheduling paradigm for high-reliability emergency communications.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Cyber-crime defenses and forensics

Abstract

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.

Problem

• Optimizing security-aware resource allocation in UAV-assisted networks
• Balancing latency minimization with energy efficiency in dynamic environments
• Addressing joint optimization gaps in existing O-RAN approaches

Innovation

• RL-based framework for dynamic UAV resource allocation
• Integrates security-aware allocation with latency minimization
• Real-time adaptation to network dynamics using reinforcement learning