UE-Based Mobility Optimization Using ML Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current mobility optimization techniques in wireless networks, such as those defined by 3GPP TSs, face challenges in predicting user equipment (UE) mobility accurately, especially in terms of preserving user privacy and optimizing handover processes.
Innovation Solution
Implementing UE-based mobility prediction using machine learning (ML) techniques, where the UE trains a model to predict the next best candidate cell or beam and notifies the network, thereby enhancing mobility robustness and preserving user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network-based mobility prediction is used, then mobility optimization can be achieved, but user privacy is compromised and prediction accuracy is reduced
Solution Approach 1:
Instead of the network collecting and analyzing UE mobility data (traditional approach), the patent inverts the approach by having the UE perform mobility prediction locally using onboard processors and machine learning models. This inversion transfers the prediction function from network-side to device-side, thereby preserving user privacy while maintaining prediction accuracy.
Solution Approach 2:
The UE is empowered to perform mobility prediction independently using its own computational resources and stored mobility information, without requiring network-side processing of personal data. The UE serves itself by generating mobility predictions locally and providing assistance information to the network only when needed, thus eliminating privacy concerns associated with network-based data collection.
2Reliability
If traditional handover procedures are used, then network control is maintained, but handover failures occur and resource utilization is suboptimal
Solution Approach 1:
The UE performs mobility prediction in advance and identifies candidate target cells before handover is actually needed. By providing assistance information containing predicted mobility patterns and candidate cells to the network beforehand, the system prepares for handover proactively, reducing handover failures and optimizing resource allocation in advance.
Solution Approach 2:
The system implements a feedback mechanism where the UE continuously provides mobility prediction results and assistance information to the network. The network uses this feedback to make informed handover decisions, and the UE refines its predictions based on actual handover outcomes and network responses, creating a closed-loop system that improves handover reliability and resource efficiency.
Data Source
AI summary
The present application relates to devices and components including apparatus, systems, and methods for user equipment (UE)-based mobility optimization are described herein, including machine learning (ML)-assisted techniques.