Core Network AI Prediction for UE Mobility and Paging

The patent implements AI prediction algorithms that forecast future UE behavior (mobility patterns, resource needs) before events occur. The core network node collects historical location and movement data, processes it through prediction algorithms, and prepares optimized resource allocation and mobility management decisions in advance, enabling proactive rather than reactive network control

The patent introduces an AI prediction layer as an intermediary between raw network data and resource management decisions. The core network node acts as a mediator that collects data from multiple sources (location information, movement patterns), processes it through prediction algorithms, and outputs optimized control decisions, thereby managing the complexity centrally rather than distributing it across the entire network

The patent applies AI prediction to forecast UE location and mobility patterns in advance of paging events. The core network node uses historical data and prediction algorithms to determine where UEs are likely to be located, enabling targeted paging in predicted locations rather than broadcasting to all possible areas, thus reducing paging time and computational waste

The patent implements location-aware paging optimization where the AI prediction algorithm focuses computational resources on specific geographic areas and time periods based on predicted UE behavior. Instead of uniform paging across the entire network, the system concentrates processing power on predicting and paging in high-probability locations, reducing overall computational overhead while improving efficiency