A Predictive-Reactive Learning Framework for Cellular-Connected UAV Handover in Urban Heterogeneous Networks

Unmanned aerial vehicles (UAVs) operating in dense urban environments often face link disruptions due to high mobility and interference. Reliable connectivity in such conditions requires advanced handover strategies. This paper presents a predictive-reactive Q-learning framework (PRQF) that optimizes handover decisions while sustaining throughput in dynamic heterogeneous urban networks. The framework combines an Extreme Gradient Boosting (XGBoost) classifier with a Q-learning agent through a probabilistic gating mechanism. UAVs follow a sinusoidal mobility model to ensure consistent and representative movement across experiments. Simulations using 3GPP-compliant Urban Macro (UMa) channel models in a 10 km × 10 km area show that PRQF achieves an average reduction of 84% in handovers at 100 km/h and 83% at 120 km/h, compared to the standard 3GPP A3 event-based handover method. PRQF also maintains a consistently high average throughput across all methods and speed scenarios. The results show better link stability and communication quality, demonstrating that the proposed framework is adaptable and scalable for reliable UAV communications in urban environments.