Multi-Dimensional UE Location Prediction for Accurate Handover
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Solution Overview
Problem
Existing radio access networks lack the ability to predict the location of user equipment at a future time point accurately, relying solely on historical location data, resulting in significant prediction deviations.
Innovation Solution
A location prediction method and apparatus that utilizes a server or base station to obtain historical location measurement information from user equipment, apply machine learning algorithms, and send notification or optimization information to improve location prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only historical location data is used for prediction, then the system complexity is low, but the prediction accuracy is poor with large deviation
Solution Approach 1:
The patent transitions from using only historical location data (one dimension) to incorporating multi-dimensional measurement information including historical location measurements, speed measurements, and Angle-of-Departure (AOD) measurements. This dimensional expansion significantly improves prediction accuracy by providing a more comprehensive view of UE movement patterns and current state.
Solution Approach 2:
The patent introduces an intermediary processing system that collects, integrates, and analyzes multi-source measurement data before generating predictions. This intermediary layer (comprising the obtaining module, determining module, and sending module) manages the complexity of processing multiple data types while delivering accurate predictions to the network side.
2Measurement precision
If multi-dimensional measurement information is incorporated, then the prediction accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The patent segments the data processing function into distinct modules: an obtaining module for collecting multi-dimensional measurements, a determining module for analyzing the data and generating predictions, and a sending module for communicating results. This segmentation manages processing complexity by organizing tasks into manageable, specialized components.
Solution Approach 2:
The determining module acts as an intermediary that processes multi-dimensional measurement information from various sources (UE reports, base station measurements) and transforms this complex data into actionable prediction results. This intermediary processing layer handles the complexity of integrating heterogeneous data types.
3Loss of information
If historical location data only is used, then the information processing is simple, but the prediction deviation is large
Solution Approach 1:
The patent enriches the information base by adding speed measurements and AOD measurements to the traditional historical location data. These additional dimensions provide critical insights into UE movement dynamics and direction, enabling more accurate predictions while maintaining organized information processing through modular architecture.
Data Source
AI summary
Provided are a location prediction method and apparatus, a node and a storage medium. The method includes: a server obtaining a measurement report message sent by a base station, where the measurement report message includes historical location measurement information reported by at least one UE and/or historical location measurement information of the at least one UE measured by the base station; and the server determining prediction location information of the at least one UE at a first time point or in a first time period according to the measurement report message, and sending notification information to the base station according to the prediction location information. By introducing measurement information in multiple dimensions, the accuracy of predicting the location of UE can be effectively improved.