Cellular Network Configuration Using AI KPI Prediction
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Solution Overview
Problem
The management of large cellular networks is challenging due to the complexity and cost associated with manual optimization of configuration parameters, leading to imprecise adjustments and suboptimal network performance.
Innovation Solution
An AI-based configuration management system that utilizes regression models to predict key performance indicators (KPIs) and automate configuration management, incorporating features selection and causality graphs to identify CM misconfigurations and recommend optimal parameter settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual optimization of configuration parameters is used, then network performance can be adjusted, but operational costs increase and precision decreases
Solution Approach 1:
The system enables self-service through automated configuration management where the network management system automatically trains regression models on historical data, performs parameter optimization, and applies configurations without manual intervention. The system serves itself by continuously learning from network performance data and autonomously adjusting configuration parameters to optimize KPIs.
Solution Approach 2:
The patent replaces manual mechanical optimization processes with an AI-based regression model system. Instead of operators manually adjusting parameters based on experience, the system uses machine learning models trained on historical network data to automatically predict and optimize configuration parameters, substituting human mechanical operations with automated computational processes.
2Productivity
If manual configuration management is used, then operational control is maintained, but processing speed decreases
Solution Approach 1:
The system performs preliminary action by pre-training regression models on extensive historical network data before actual optimization is needed. The model learns from past configurations and performance outcomes, so when optimization is required, the system can quickly predict optimal parameters without time-consuming manual analysis or trial-and-error processes.
Solution Approach 2:
The automated system performs configuration optimization without requiring operator time or manual processing. The regression model independently analyzes network data, identifies optimization opportunities, and applies configuration changes, freeing operational resources and accelerating the overall configuration management process.
3Extent of automation
If AI-based regression models are implemented, then automation increases and productivity improves, but system complexity increases
Solution Approach 1:
The regression model system performs multiple functions within a single unified framework: it trains on historical data, predicts optimal configurations, evaluates KPI impacts, and applies optimizations. This multi-functional approach consolidates what would otherwise require separate manual processes into one automated system, managing complexity through functional integration rather than proliferation of separate components.
Data Source
AI summary
Methods and apparatuses for automating configuration management in cellular networks. A method of a UE comprises: training, based on historical samples, a regression model y using samples obtained from a set of parameters, wherein the regression model y comprises a function of a first term X and a second term h; and predicting, based on the regression model y, a target KPI to capture parameter impacts corresponding to the second term h.