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

TL;DR

This paper presents a deep learning-based predictive handover strategy for 6G networks that improves mobility management by accurately forecasting serving cells, reducing retraining time, and supporting dynamic network adjustments.

Contribution

It introduces a novel deep learning algorithm utilizing transfer learning for predicting handovers, compliant with O-RAN standards, and capable of dynamic network adaptation.

Findings

Achieves 92% accuracy in predicting future serving cells.

Reduces retraining time by up to 91% with transfer learning.

Supports dynamic network adjustments with high prediction reliability.

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.