5G Handover Success Prediction via Machine Learning Clustering
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Solution Overview
Problem
The increasing number of small cells in 5G wireless networks leads to a higher number of handovers, making traditional manual settings of handover parameters inefficient and inaccurate, resulting in poor user experience and resource wastage due to handoff and radio link failures.
Innovation Solution
The use of machine learning and artificial intelligence to analyze handover data, cluster cells with similar behaviors, predict future handover success rates, and automatically update network parameters to improve handover success rates without manual operator intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of small cells is increased to meet traffic demands, then network capacity and coverage are improved, but the number of handovers increases leading to more handoff failures and radio link failures
Solution Approach 1:
The system performs preliminary actions by predicting future handover success rates using machine learning models before handovers occur. Historical handover data is analyzed to forecast upcoming failures, allowing the system to proactively adjust handover parameters and configure cells to prevent failures before they happen, rather than reacting after failures occur.
Solution Approach 2:
The system implements dynamic adjustment of handover parameters based on predicted failure patterns. Instead of static manual configuration, the machine learning model continuously forecasts handover success rates and automatically updates handover parameters in real-time to adapt to changing network conditions, traffic patterns, and cell behaviors.
2Measurement precision
If manual setting of handover parameters is performed for each cell, then parameter accuracy may be improved, but significant manpower and time are required making the process inefficient
Solution Approach 1:
The system enables self-service by allowing the network to automatically configure and optimize its own handover parameters through machine learning. The ML model autonomously analyzes historical data, predicts future performance, and adjusts parameters without human intervention, making the network self-optimizing and eliminating the need for manual operator configuration.
Solution Approach 2:
The patent replaces the mechanical manual process of parameter setting with an automated intelligent system. Instead of operators manually analyzing data and configuring parameters cell-by-cell, a machine learning model automatically performs prediction and optimization, substituting human manual work with computational intelligence that processes data faster and more accurately.
3Ease of operation
If traditional manual methods are used to set handover parameters, then operational simplicity is maintained, but the process becomes inefficient and inaccurate for large numbers of small cells
Solution Approach 1:
The machine learning system provides a universal solution that handles parameter optimization for all cells simultaneously through a single automated process. Instead of requiring separate manual configuration for each cell, the ML model processes multiple cells in parallel, applying learned patterns across the entire network to achieve both simplicity and high productivity.
Solution Approach 2:
The system automatically changes handover parameters based on data-driven insights from the machine learning model. Instead of relying on operator judgment and manual adjustment, the system dynamically modifies parameters such as handover thresholds, timing, and cell configuration based on predicted failure patterns, achieving both ease of operation and improved efficiency.
Data Source
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AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to predict future handover success rates of cells of a wireless network and to determine updated network parameter values that may be used to improve the future handover success rates. Clustering algorithms may be applied to handover success rates associated with the cells to group the cells into different clusters. Machine learning (ML) models may be trained based on historical handover data associated with cells of the clusters to predict future handover success rates based on current handover success rates. The output of the ML models may be used to determine one or more updated network parameter values to be implemented by the wireless network to improve future handover success rates at one or more cells.