xApp Empowered Resource Management for Non-Terrestrial Users in 5G O-RAN Networks

TL;DR

This paper presents a proactive UAV mobility management xApp using reinforcement learning with transfer learning to optimise handovers in 5G O-RAN networks, reducing handover events significantly.

Contribution

It introduces a novel predictive framework employing DDQN RL and transfer learning for UAV mobility management in O-RAN, enhancing network reliability and reducing handovers.

Findings

Reduces handover events by up to 54.6% compared to greedy approaches.

Maintains outage probability at negligible levels.

Achieves a favourable trade-off between handover frequency and connectivity reliability.

Abstract

This paper introduces a proactive Unmanned Aerial Vehicle (UAV) mobility management xApp for Open Radio Access Network (O-RAN) Near Real-Time Radio Intelligent Controller (Near-RT RIC) environments, employing Double Deep Q-Network (DDQN) reinforcement learning (RL) enhanced with transfer learning to optimise handover decisions for UAVs operating along predetermined flight trajectories. Unlike reactive approaches that respond to signal degradation, the proposed framework anticipates network conditions and minimises both outage probability and handover frequency through predictive optimisation. The system leverages centralised weight averaging to consolidate knowledge from multiple flight scenarios into a global model capable of generalising to previously unseen operational environments without extensive retraining. A comprehensive evaluation demonstrates that the proposed framework achieves a favourable trade-off between handover frequency and connectivity reliability, reducing handover events by up to 54.6% compared to greedy approaches while maintaining outage probability at practically negligible levels. The results validate the effectiveness of intelligent learning-based approaches for UAV mobility management in next-generation O-RAN architectures, thereby contributing to seamless integration of aerial user equipment into cellular networks.