AI-Based Radio Resource Management for Predictive Handover
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Solution Overview
Problem
Conventional L3 handover mechanisms in wireless communication systems face challenges with diverse types of terminals and services, leading to undesirable events such as handover failures and service interruptions, as they may not adapt effectively to varying mobility scenarios.
Innovation Solution
Implementing AI/ML-based prediction and reporting for radio resource management by enhancing UE capabilities to perform AI/ML inference during configured measurement gaps, allowing for predictive mobility management through additional information exchange with the base station.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional L3 handover mechanisms are used, then network coverage is maintained, but handover failures and service interruptions occur due to inability to adapt to varying mobility scenarios
Solution Approach 1:
The patent applies AI/ML models to perform predictive handover decisions before actual handover events occur. The system predicts future mobility patterns and network conditions, initiating handover preparations in advance to prevent handover failures and service interruptions caused by reactive conventional mechanisms.
Solution Approach 2:
The system continuously monitors measurement reports from UEs and uses this feedback to train and update AI/ML models. This closed-loop feedback mechanism enables the system to learn from actual mobility patterns and improve prediction accuracy, thereby enhancing both handover reliability and adaptability to varying scenarios.
2Measurement precision
If AI/ML based prediction and measurement reporting is implemented, then mobility performance and handover accuracy are improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the AI/ML processing into two parts: lightweight measurement and data collection at the UE side, and complex prediction modeling at the network side. This segmentation reduces the processing burden on UEs while maintaining high handover decision accuracy through network-based AI/ML inference.
3Speed
If measurement reporting is performed continuously, then real-time mobility management is achieved, but energy consumption and signaling overhead increase
Solution Approach 1:
The system uses AI/ML predictions to determine optimal measurement reporting triggers in advance, rather than continuous monitoring. This allows the network to anticipate when measurements are needed based on predicted mobility patterns, reducing unnecessary measurements and energy consumption while maintaining timely handover decisions.
Solution Approach 2:
The patent implements periodic measurement reporting configured by the network based on predicted mobility scenarios. Instead of continuous reporting, measurements are performed at optimized intervals determined by AI/ML predictions, balancing real-time responsiveness with energy efficiency and reduced signaling overhead.
Data Source
AI summary
A method and apparatus to support efficient radio resource management is provided. The method includes receiving from a base station an RRReconfiguration message that comprises various parameters for AIML based prediction, performing measurement and AIML based prediction according to measurement window and prediction window and initiating measurement reporting procedure based on AIML based prediction.