ML-Based Handover Prediction for 5G Beam Signal Fluctuations
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Solution Overview
Problem
Current wireless communication systems face challenges in accurately determining handover decisions due to rapid signal strength fluctuations and measurement uncertainties, leading to suboptimal handover timing and potential service interruptions, especially in 5G networks where 'Too Late' handovers are prevalent, causing radio link failures and inefficient mobility performance.
Innovation Solution
A machine learning-based solution that determines the difference in signal strength between serving and non-serving cell beams, using these differences and timing advance values as inputs to train machine learning models to predict when a measurement report should be sent and whether a conditional handover should be performed, thereby optimizing handover timing and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional handover methods are used based on signal strength thresholds, then the system is simple to implement, but handover timing is inaccurate leading to 'Too Late' handovers and radio link failures
Solution Approach 1:
The patent transforms the handover decision process by changing from simple signal strength threshold comparison to a machine learning-based prediction approach. The ML model processes multiple input parameters (signal strength differences, timing advance values, beam measurements) to predict optimal handover timing, fundamentally altering the decision-making parameters from reactive threshold-based to proactive prediction-based.
Solution Approach 2:
The machine learning model performs preliminary prediction of handover timing before the actual handover event occurs. By analyzing historical and real-time measurement data, the system predicts the optimal handover moment in advance, allowing the network to prepare handover commands and resources beforehand, thus avoiding 'Too Late' handovers.
2Reliability
If machine learning models are deployed for handover prediction, then handover accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The user equipment performs self-learning by training the machine learning model locally using measurement data collected during normal operation. This self-service approach eliminates the need for continuous network-side training and deployment updates, reducing network computational burden and enabling the UE to adapt to its specific radio environment independently.
Solution Approach 2:
The patent introduces an intermediate layer of machine learning prediction between raw measurement data and handover decisions. Instead of directly comparing signal strengths, the ML model acts as an intermediary that processes measurements, identifies patterns, and outputs predicted handover timing, thereby improving reliability while managing computational complexity through intelligent abstraction.
3Measurement precision
If measurement reporting is increased to improve handover decisions, then measurement precision improves, but signaling overhead and network load increase
Solution Approach 1:
The system applies partial measurement reporting by selectively reporting only the most relevant measurements to the machine learning model at the user equipment. Instead of reporting all raw measurement data, the UE processes measurements locally and reports only the processed features and predictions that are essential for handover decision-making, reducing signaling overhead while maintaining prediction accuracy.
Data Source
AI summary
There is provided an apparatus comprising means for determining, at a user equipment, a difference between signal strength for a first beam of a serving cell of a network and signal strength for a second beam of the serving cell, means for providing the determined difference as an input for a machine learning model, wherein the output of the machine learning model is numerical data or categorical data, means for determining, based on the output of the machine learning model, that a measurement report should be provided to the network and means for providing the measurement report to the network.