AI Handover Prediction for Reliable 5G/6G Cell Switching
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Solution Overview
Problem
Existing handover mechanisms in wireless communication systems, such as 5G and beyond, face issues like handover failure, radio link failure, and throughput loss, particularly in high-mobility scenarios and dense cellular environments, due to their reactive nature.
Innovation Solution
Implementing an artificial intelligence (AI) model for predicting handover events, enabling proactive handover decisions based on machine learning algorithms that utilize past and future cell measurement information to optimize handover timing and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive handover mechanisms are used, then the system is simple to implement, but handover failure and radio link failure occur frequently in high-mobility scenarios
Solution Approach 1:
The patent applies preliminary action by using AI models to predict future handover events before they occur. The system performs handover predictions based on historical measurement data and mobility patterns, enabling proactive handover preparation and execution timing optimization, which significantly reduces handover failures in high-mobility scenarios compared to traditional reactive mechanisms.
Solution Approach 2:
The patent introduces an AI model as an intermediary between the measurement data collection and handover decision-making processes. This intermediary analyzes patterns in measurement data and generates predictive insights that improve handover reliability, acting as a smart mediator that enhances decision-making without requiring complete system redesign.
2Loss of time
If AI-based prediction is implemented, then handover timing is optimized, but information processing requirements increase
Solution Approach 1:
The patent extracts only the essential features and patterns from measurement data that are relevant for handover prediction. The AI model is trained to identify and process only the critical information needed for accurate timing predictions, filtering out redundant data and reducing the overall information processing load while maintaining high timing accuracy.
Solution Approach 2:
The patent transforms raw measurement data into optimized prediction parameters through AI processing. By changing the representation of data from raw measurements to predicted handover timing parameters, the system reduces information processing requirements while improving timing accuracy for handover execution.
3Productivity
If proactive handover decisions are made, then throughput loss is reduced, but prediction accuracy requirements increase
Solution Approach 1:
The patent implements feedback mechanisms where the AI model continuously learns from actual handover outcomes and measurement data. The system refines its prediction accuracy by incorporating feedback from successful and unsuccessful handover attempts, enabling progressively better throughput maintenance while adapting to varying mobility patterns and network conditions.
Solution Approach 2:
The patent applies dynamics by making the prediction model adaptive and flexible. The AI system dynamically adjusts its prediction parameters and confidence thresholds based on current network conditions and mobility patterns, allowing proactive handover decisions to maintain throughput effectively while accommodating varying accuracy requirements in different scenarios.
Data Source
AI summary
The disclosure relates to a 5th generation (5G) or 6th generation (6G) communication system for supporting a higher data transmission rate. A method performed by a terminal in a wireless communication system is provided. The method includes receiving, from a base station, first information on a configuration for a prediction, based on the first information, performing the prediction, transmitting, to the base station, second information on the prediction, and receiving, from the base station, third information on a target cell, wherein the second information is used for determining a timing associated with a handover.