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

arXiv: https://arxiv.org/abs/2510.18084 · HTML: https://mlsanity.com/paper/2510.18084/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Toward Next Generation Open Radio Access Network--What O-RAN Can and Cannot Do!](https://mlsanity.com/paper/2111.13754/) (2021)
- [UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization](https://mlsanity.com/paper/2008.00218/) (2020)
- [Efficient Resource Management for Secure and Low-Latency O-RAN Communication](https://mlsanity.com/paper/2503.07857/) (2025)

## Abstract (arXiv)

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.