Data-Driven Design of 3GPP Handover Parameters with Bayesian Optimization and Transfer Learning

Abstract: Mobility management in dense cellular networks is challenging due to varying user speeds and deployment conditions. Traditional 3GPP handover (HO) schemes, relying on fixed A3-offset and time-to-trigger (TTT) parameters, struggle to balance radio link failures (RLFs) and ping-pongs. We propose a data-driven HO optimization framework based on high-dimensional Bayesian optimization (HD-BO) and enhanced with transfer learning to reduce training time and improve generalization across different user speeds. Evaluations on a real-world deployment show that HD-BO outperforms 3GPP set-1 and set-5 benchmarks, while transfer learning enables rapid adaptation without loss in performance. This highlights the potential of data-driven, site-specific mobility management in large-scale networks.