ML Classification for Wireless Network Mobility Optimization
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Solution Overview
Problem
Traditional methods for optimizing mobility in LTE networks are limited by their reliance on manual analysis and pre-determined thresholds, leading to suboptimal configurations and a single default strategy for all network elements, which fails to address the complexity and variability of radio environments effectively.
Innovation Solution
A computing device uses a classification model to generate network configuration recommendations by grouping network elements with similar radio environments and applying machine learning techniques to optimize mobility performance, reducing the need for manual analysis and enabling more detailed tuning of layer management configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual analysis and pre-determined thresholds are used for mobility optimization, then the process is simple and easy to operate, but the optimization precision and adaptability to different radio environments are insufficient
Solution Approach 1:
The patent replaces manual analysis and rule-based threshold methods with a machine learning classification model. The system automatically processes network performance metrics, radio environment data, and configuration parameters to generate mobility optimization recommendations, eliminating the need for manual intervention while achieving higher precision through data-driven insights
Solution Approach 2:
The system changes the approach from using fixed pre-determined thresholds to dynamically generated recommendations based on multiple input parameters including network performance metrics, radio environment characteristics, and current configuration settings. The classification model processes these varying parameters to produce context-specific optimization recommendations
2Adaptability or versatility
If a single default mobility strategy is applied to all network elements, then the configuration process is simplified, but the adaptability to different radio environments and network scenarios is reduced
Solution Approach 1:
The patent segments the network into different groups based on radio environment characteristics, network performance metrics, and configuration patterns. The classification model identifies distinct clusters of network elements with similar characteristics, allowing tailored mobility strategies to be generated for each segment rather than applying a uniform approach to all elements
Solution Approach 2:
The system applies local quality by generating specific mobility optimization recommendations for each network element or segment based on its unique radio environment and performance characteristics. Each recommendation is customized to the local conditions of the specific network element rather than applying a global default strategy
3Measurement precision
If comprehensive network performance metrics and radio environment data are analyzed, then the optimization accuracy is improved, but the data processing complexity and time requirements increase
Solution Approach 1:
The system performs preliminary action by pre-processing and organizing network performance metrics and radio environment data before the optimization process. The classification model is trained in advance on historical data, enabling it to quickly process new inputs and generate recommendations without requiring extensive real-time analysis of all raw data
Solution Approach 2:
The patent replaces time-consuming manual data analysis with an automated machine learning system that can rapidly process comprehensive network metrics and radio environment data. The classification model efficiently handles large datasets and generates optimization recommendations much faster than traditional manual methods
Data Source
AI summary
A computing device (110) generates, from a network graph representing a plurality of network elements in a wireless communication network (100) and based on a plurality of network performance metrics, a model of the wireless communication network (100) that groups the network elements having similar radio environments (170) together. The computing device (110) also generates a network configuration recommendation (370) for at least one of the network elements based on the model.