  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.
