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.

翻译：下一代蜂窝网络将演变为更复杂和虚拟化的系统，采用机器学习进行增强优化，并利用更高频段和更密集的部署来满足多样化的服务需求。这一演进在带来诸多优势的同时，也将带来挑战，尤其是在移动性管理方面，因为更小的覆盖范围和更高的信号衰减将增加整体切换次数。为应对这些挑战，我们提出一种基于深度学习的算法，利用顺序用户设备测量来预测未来服务小区，以最小化切换失败和中断时间。我们的算法使网络运营商能够动态调整切换触发事件，或引入无人机基站以增强覆盖和容量，并通过迁移学习技术优化负载均衡和能效等网络目标。我们的框架符合O-RAN规范，可作为xApp利用E2SM-KPM服务模型部署在近实时无线接入网络智能控制器中。评估结果表明，我们的算法在预测未来服务小区时以高概率达到92%的准确率。最后，通过采用迁移学习，当网络动态引入新的切换触发决策或无人机基站时，我们的算法将重训练时间显著减少了91%和77%。