UE Mobility Prediction Signaling for Reliable Handover
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately predicting user equipment (UE) mobility, leading to inefficiencies in handover procedures, increased resource consumption, and potential service disruptions due to outdated and incomplete mobility history data.
Innovation Solution
Implementing signaling mechanisms to transfer and store mobility history data within the core network, enabling continuous training and refinement of UE mobility prediction models, ensuring up-to-date and accurate predictions for improved handover management and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mobility history data is stored outside the core network, then system architecture simplicity is maintained, but prediction accuracy deteriorates due to outdated and inaccurate data
Solution Approach 1:
The patent segments the mobility history data management function by introducing a dedicated Network Data Analytics Function (NWDAF) that operates independently within the core network. This segmentation allows the core network to maintain architectural clarity while collecting and processing mobility data separately from traditional network elements, thereby improving prediction accuracy without proportionally increasing overall system complexity.
Solution Approach 2:
The NWDAF acts as an intermediary component between various network functions and the mobility prediction system. It receives mobility history data from network nodes, processes and analyzes it to generate predictions, and delivers results to relevant network elements. This intermediary role enables accurate predictions while keeping the core network architecture modular and manageable.
2Measurement precision
If mobility history data is collected and processed in real-time, then prediction accuracy is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous real-time processing, the system implements periodic data collection and model training cycles. The NWDAF aggregates mobility history data at scheduled intervals, trains prediction models periodically, and updates network nodes accordingly. This periodic approach maintains prediction accuracy while significantly reducing the energy consumption associated with constant data processing.
Solution Approach 2:
The mobility prediction model leverages historical data patterns and statistical models to generate predictions autonomously without requiring constant external computational resources. Once trained, the model can perform self-inferred predictions using stored mobility history, reducing the need for continuous energy-intensive processing while maintaining accurate predictions.
3Reliability
If handover decisions are made based on outdated mobility history data, then system response time is reduced, but handover failure rate increases
Solution Approach 1:
The system performs preliminary actions by collecting and storing mobility history data in advance, and by pre-training prediction models using historical patterns. The NWDAF continuously aggregates and pre-processes mobility data, so when handover decisions are needed, the model can quickly generate predictions based on pre-collected data without requiring real-time data gathering, thus reducing decision time while maintaining reliability.
Solution Approach 2:
The system implements feedback mechanisms where handover outcomes and network performance metrics are fed back into the NWDAF to refine and retrain prediction models. This feedback loop continuously improves the accuracy of mobility predictions, ensuring that handover decisions are based on increasingly accurate and up-to-date models, thereby reducing handover failures over time.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first network node in a radio access network (RAN) may transmit mobility history data for a user equipment (UE) to a second network node in a core network associated with the RAN. The first network node may receive a UE mobility prediction model that is based at least in part on the mobility history data from the second network node. Numerous other aspects are described.