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

TL;DR

This paper introduces a reinforcement learning framework for UAV-assisted O-RAN that optimizes security, latency, and energy efficiency in disaster response scenarios, adapting dynamically to network changes.

Contribution

It presents a novel RL-based resource allocation method that jointly considers security, latency, and energy efficiency in UAV-assisted O-RAN networks, addressing a gap in existing approaches.

Findings

Outperforms heuristic baselines in simulations

Achieves enhanced security and energy efficiency

Maintains ultra-low latency in dynamic environments

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.