用强化学习提前预测无人机切换，减少54.6%掉线风险
xApp Empowered Resource Management for Non-Terrestrial Users in 5G O-RAN Networks
- 基于双深度Q网络+迁移学习，提前优化无人机切换决策
- 相比传统方法减少54.6%切换次数，掉线率几乎为零
- 适合5G O-RAN中无人机等高空用户接入场景
本文提出一种面向开放无线接入网（O-RAN）近实时无线智能控制器（Near-RT RIC）环境的主动式无人机（UAV）移动性管理xApp，采用增强迁移学习的双深度Q网络（DDQN）强化学习，优化沿预设航线飞行的无人机手投决策。与响应信号劣化的被动方法不同，该框架通过预测性优化提前应对网络状况，显著降低中断概率和切换频率。系统通过中心化权重平均，将多个飞行场景的知识融合为全局模型，实现对未见运行环境的泛化能力而无需大量重训练。全面评估表明，该框架在切换频率与连接可靠性间取得良好权衡：相比贪婪策略，切换事件减少高达54.6%，同时维持近乎可忽略的中断概率。结果验证了基于智能学习的无人机移动性管理在下一代O-RAN架构中的有效性，推动空中用户设备无缝融入蜂窝网络。
原文摘要 · Abstract (English)
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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