xApp Empowered Resource Management for Non-Terrestrial Users in 5G O-RAN Networks
Mohammed M. H. Qazzaz, Syed Ali Zaidi, Aubida A. Al-Hameed, Abdelaziz Salama, Des Mclernon
TLDR
This paper proposes a DDQN xApp for proactive UAV mobility management in 5G O-RAN, reducing handovers and outages.
Key contributions
- Introduces a proactive UAV mobility xApp for O-RAN Near-RT RIC environments.
- Employs Double Deep Q-Network (DDQN) with transfer learning for optimized handover decisions.
- Predictively minimizes outage probability and handover frequency, reducing events by up to 54.6%.
- Leverages centralized weight averaging for global model generalization to unseen flight scenarios.
Why it matters
This paper introduces a proactive, AI-driven solution for managing UAVs in 5G O-RAN, a critical step for integrating aerial users into cellular networks. It overcomes limitations of reactive systems by predicting network conditions, significantly reducing handovers and outages. This work validates intelligent learning for next-gen O-RAN architectures.
Original 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.
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