AI-Driven Mobility Prediction for Efficient Session Continuity

Maintaining active sessions becomes uniquely strenuous in the case of user movement due to user expectations of connection and seamless handoff during the transition to 5G mobile networks. This paper addresses issues related to session continuity by predicting movement across cell borders using neural mobility predictors, thereby enhancing the user experience. The proposed system, which utilizes real mobility data and employs advanced predictive models such as LSTM and Transformer networks, enables accurate user trajectory prediction, thereby enhancing the efficiency of handover execution. Actual simulation results showcase remarkable improvements in overall session quality, handover success rate, and latency compared to the heuristic model. Proactive mobility prediction and advanced session control are essential for ensuring seamless service continuity within ultra-dense 5G infrastructure environments. These conclusions offer fundamental insights into the architecture of future networks, designed to meet the demanding needs of emerging use cases, including autonomous vehicles, augmented and virtual reality experiences, and telepresence.