UE Mobility Reporting Based on Prediction for Conditional Handovers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mobility management systems in LTE and NR rely on network-based prediction of handover events, which leads to increased uplink signaling and power consumption due to frequent measurement reports, and may result in outdated or incomplete mobility information, especially in scenarios where UE mobility is unpredictable.
Innovation Solution
The UE predicts mobility information and reports it to the serving network node, allowing for up-to-date mobility management without frequent periodic reports, and enables conditional handovers with optimized target cell configurations based on UE-side prediction models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the network performs frequent measurement reports to obtain mobility information, then mobility management accuracy is improved, but uplink signaling overhead and power consumption increase
Solution Approach 1:
Instead of the UE reporting measurements frequently to the network, the network performs predictions and sends only when updates are needed. This inverts the traditional reporting direction and timing, reducing UE power consumption while maintaining mobility management accuracy.
Solution Approach 2:
The network performs mobility predictions in advance using stored measurement data and prediction algorithms. By preparing predictions beforehand and only transmitting when changes occur, the system avoids frequent uplink signaling and reduces UE power consumption.
2Measurement precision
If the network performs frequent measurement reports to obtain mobility information, then mobility management accuracy is improved, but uplink signaling overhead increases
Solution Approach 1:
The network performs mobility predictions in advance using stored measurement data and prediction algorithms. By preparing predictions beforehand and only transmitting when changes occur, the system avoids frequent uplink signaling and reduces UE power consumption.
Solution Approach 2:
Instead of the UE reporting measurements frequently to the network, the network performs predictions and sends only when updates are needed. This inverts the traditional reporting direction and timing, reducing uplink signaling overhead.
3Productivity
If the network uses traditional handover prediction methods, then handover decisions are made, but mobility information may be outdated or incomplete in unpredictable mobility scenarios
Solution Approach 1:
The system changes the approach from reactive measurement reporting to proactive prediction using machine learning algorithms. By incorporating multiple parameters (historical mobility patterns, current signal quality, UE speed) into prediction models, the network obtains more complete and timely mobility information even for unpredictable scenarios.
Solution Approach 2:
The network continuously updates prediction models using feedback from actual handover outcomes and measurement data. This feedback mechanism ensures that mobility information remains accurate and complete by constantly refining predictions based on real-world performance.
Data Source
AI summary
The present disclosure relates to a method performed by a User Equipment (UE) for handling mobility information in a communications network. The UE predicts the mobility information. The UE reports the predicted mobility information to a first network node currently serving the UE in the communications network.