Towards smart 6G: Mobility prediction for dynamic edge services migration

• Large-scale 5G mobility dataset generation via a comprehensive simulation workflow. • Predictive handover: Development of an LSTM model to forecast future serving cells. • Proactive tracking: High accuracy trajectories in variable traffic density scenarios. • Smart migration: proactive decisions for low latency and high resource efficiency. In this work, we address the challenge of maintaining high Quality of Service (QoS) of edge services provided to constantly moving devices, especially in urban environments such as connected vehicles. To support proactive service migration and maintain low latency in these environments we propose a long short-term memory model that predicts the end device trajectory in terms of future base stations. In our work, we have used traffic data from urban environments combined with wireless channel metrics from cellular networks to be able to generate an efficient predictive model that works well with varying traffic densities. Our experimental results show that the proposed model offers high accuracy in predicting an array of future coverage cells while remaining resilient to constantly changing traffic volumes and patterns. This study highlights the importance of unifying artificial intelligence and machine learning for dynamic resource orchestration and ensuring QoS in real-time applications operating on smart 6G networks.