Predictive UE Location Models for Wireless Network Signaling Reduction
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Solution Overview
Problem
Current wireless network mobility management systems face inefficiencies in predicting user equipment (UE) registration areas, leading to increased control signaling and resource consumption when UEs move between registration areas.
Innovation Solution
The implementation of predictive UE location models using artificial intelligence/machine learning techniques, which analyze UE location history and attributes to accurately predict registration areas, reducing the need for repeated registration procedures and minimizing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional mobility management systems are used for UE registration, then the system can handle basic mobility requirements, but control signaling overhead and resource consumption increase when UEs move between registration areas
Solution Approach 1:
The system performs preliminary actions by predicting future UE registration areas before the UE actually moves to them. Machine learning models analyze historical location data and UE attributes to pre-determine likely registration areas, allowing the network to prepare in advance and reduce signaling overhead when movements occur.
Solution Approach 2:
The system implements feedback mechanisms where actual UE movement patterns are continuously monitored and fed back to the machine learning models. This feedback loop allows the models to refine their predictions over time, improving accuracy while optimizing resource allocation and reducing control signaling.
2Measurement precision
If predictive location determination using AI/ML techniques is implemented, then prediction accuracy of UE registration areas improves, but system complexity increases
Solution Approach 1:
The patent introduces intermediary components such as location management functions and machine learning model repositories that mediate between raw UE data and prediction outputs. These intermediaries simplify the overall system architecture by centralizing complex AI/ML operations and providing standardized interfaces for location prediction services.
3Measurement precision
If frequent registration updates are required for moving UEs, then location accuracy is maintained, but control signaling overhead increases
Solution Approach 1:
The system performs preliminary prediction of UE locations to determine which registration area updates are actually necessary. By predicting future locations with high accuracy, the system can skip unnecessary registration updates and only perform them when the UE is likely to have moved to a different registration area, reducing signaling overhead while maintaining location accuracy.
Data Source
AI summary
A system described herein may provide a technique for the predictive determination of regions, such as registration areas associated with a wireless network, with respect to a User Equipment (“UE”) that registers with the wireless network. Such registration areas may be associated with different services, Quality of Service (“QoS”) parameters, network slices, or the like. When registering with the wireless network, the UE may receive an indication of a registration area that includes multiple tracking areas, which include a tracking area in which the UE is currently located and one or more other tracking areas in which the UE is likely to enter, as predicted based on one or more artificial intelligence/machine learning (“AI/ML”) models. The prediction may be based on the current location of the UE, UE attributes or parameters, and/or historical location information associated with the UE and/or other UEs.