Hybrid Bi-LSTM and Deep Reinforcement Learning for AI-Native Predictive Mobility Management in High-Mobility 5G/6G Networks

Reliable mobility management is critical for high-mobility 5G/6G applications such as smart cities, intelligent transportation systems (ITS), and connected vehicles, where reactive 3GPP handover mechanisms become unreliable as user equipment (UE) velocity increases. This paper proposes a hybrid Bidirectional LSTM (Bi-LSTM) and Deep Reinforcement Learning (DRL) framework for AI-native predictive handover management: a Bi-LSTM encoder extracts a knowledge embedding and forecasts future network observations, forming a predictive state that a Dueling Double DQN (Dueling DDQN) with prioritized experience replay uses to select handover actions. A closed-loop, dual-frequency refresh mechanism periodically updates both modules from accumulated network experience, without manual retuning. The framework is evaluated via Monte Carlo simulation across Smart City, ITS, and Connected Vehicle scenarios and Urban, Suburban, and Mixed eployments. The proposed Bi-LSTM predictor reduces RMSE by 51.9–68.6% relative to a persistence baseline and by 8.4–25.6% relative to a Transformer predictor, with the largest gains under high mobility. End-to-end evaluation shows the framework achieves a throughput of 67.0 Mbps, a latency of 48.1 ms, a handover failure rate of 3.47%, and an average utility of 0.678, outperforming all ablation variants; disabling closed-loop refresh causes the largest degradation (throughput loss: 7.0%, higher failure rate: 33.1%). Generalization experiments show bounded degradation under unseen conditions, with closed-loop adaptation recovering utility from approximately 0.588 to 0.678 within 100 refresh epochs after a domain shift. These results show that integrating predictive knowledge extraction, adaptive decision-making, and continual refresh provides a robust, closed-loop architecture for AI-native mobility management for proactive 5G/6G mobility management.