Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach

TL;DR

The paper tackles mobility management and handover in dense 6G+ networks by introducing a predictive framework that forecasts the future serving cell from temporal UE measurements. It adopts an Encoder–Stacked LSTM–Decoder architecture, framed as a multi-class classification problem, and is designed as an xApp for the near-Real-Time RIC using E2SM-KPM to enable closed-loop control. Key contributions include a methodology for predictive handover within O-RAN, a learning-based algorithm that outperforms baseline approaches, and a scalable architecture that supports dynamic input/output sizes with transfer learning to reduce retraining time. Empirical results show high predictive accuracy (around $92\%$) and substantial reductions in handovers and retraining overhead, demonstrating practical viability for agile, Open-RAN mobility optimization with UAV-enabled capacity enhancements.

Abstract

Next-generation cellular networks will evolve into more complex and virtualized systems, employing machine learning for enhanced optimization and leveraging higher frequency bands and denser deployments to meet varied service demands. This evolution, while bringing numerous advantages, will also pose challenges, especially in mobility management, as it will increase the overall number of handovers due to smaller coverage areas and the higher signal attenuation. To address these challenges, we propose a deep learning based algorithm for predicting the future serving cell utilizing sequential user equipment measurements to minimize the handover failures and interruption time. Our algorithm enables network operators to dynamically adjust handover triggering events or incorporate UAV base stations for enhanced coverage and capacity, optimizing network objectives like load balancing and energy efficiency through transfer learning techniques. Our framework complies with the O-RAN specifications and can be deployed in a Near-Real-Time RAN Intelligent Controller as an xApp leveraging the E2SM-KPM service model. The evaluation results demonstrate that our algorithm achieves a 92% accuracy in predicting future serving cells with high probability. Finally, by utilizing transfer learning, our algorithm significantly reduces the retraining time by 91% and 77% when new handover trigger decisions or UAV base stations are introduced to the network dynamically.

Figures (4)

• Figure 1: Our proposed predictive handover algorithm is implemented as an xApp in the near-RT RIC.
• Figure 2: Model performance across various network and model configurations $(N, K, W)$. The top left subplot (a) shows the accuracy, the top right (b) presents the precision, the bottom left (c) illustrates the recall, and the bottom right (d) displays the F1 Score for all models.
• Figure 3: Reduction in handovers achieved by our method compared to the standard 3GPP algorithm across different temporal window configurations $(K, W)$.
• Figure 4: Reduction in our model's retraining time.