AI Configuration Management for Cellular Network Anomaly Detection
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Solution Overview
Problem
The complexity of modern cellular networks makes their management costly and challenging, with manual configuration changes often leading to errors and performance degradation due to the vast number of configuration parameters and the need for extensive human effort, resulting in high operational costs and prolonged downtimes.
Innovation Solution
An AI-based configuration management system that monitors network performance, detects anomalies, and performs automatic root cause analysis, identifying misconfigurations and recommending remedial actions or triggering self-healing processes to optimize cell performance without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration changes are performed by human operators, then configuration adjustments can be made with human judgment and adaptability, but operational costs increase and human errors lead to performance degradation
Solution Approach 1:
The system implements self-service through automated configuration management where the network management system automatically detects anomalies, analyzes root causes, and applies configuration changes without human intervention. The system monitors network performance metrics, identifies misconfigurations through pattern recognition, and executes remedial actions autonomously, eliminating the need for manual operator intervention while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical human-operated configuration system with an automated electronic system. Instead of human operators manually analyzing network metrics and making configuration changes, the system uses automated algorithms, machine learning models, and electronic configuration management tools to detect issues and apply fixes, thereby reducing operational costs and eliminating human error.
2Productivity
If the size of cellular networks doubles with 5G systems, then network capacity and coverage are improved, but management costs rise significantly
Solution Approach 1:
The system implements a universal configuration management platform that handles multiple network functions through a single automated system. The same system that detects configuration errors also performs root cause analysis, generates remediation scripts, and executes configuration changes across diverse network elements including 4G and 5G infrastructure, thereby managing expanded network capacity without proportionally increasing management costs.
Solution Approach 2:
The system employs continuous feedback mechanisms by monitoring network performance metrics in real-time, comparing actual performance against expected benchmarks, and automatically triggering configuration adjustments when deviations are detected. This closed-loop feedback system enables the network to self-optimize and maintain efficient operation as it scales, preventing cost overruns associated with manual management of expanded capacity.
3Ease of operation
If extensive human effort is applied to configuration management, then complex configuration issues can be addressed with expert judgment, but downtimes are prolonged
Solution Approach 1:
The system performs preliminary actions by pre-configuring remediation scripts and maintaining a library of known configuration issues and their solutions. When an anomaly is detected, the system immediately retrieves and applies the appropriate pre-prepared fix, eliminating the time required for human operators to diagnose and resolve the issue. The system also proactively monitors for potential configuration errors before they cause service disruptions.
Solution Approach 2:
The automated system performs self-service by independently detecting configuration anomalies, analyzing root causes using pattern recognition algorithms, and executing configuration changes without human intervention. This self-service capability dramatically reduces downtime compared to manual processes, as the system can identify and resolve issues in minutes rather than the hours required for human operators to investigate and fix problems.
Data Source
AI summary
Apparatuses and methods for identifying network anomalies. A method includes determining a cumulative anomaly score over a predefined time range based on a subset of historical PM samples and determining an anomaly ratio of a first time window and a second time window, based on the cumulative anomaly score. The method also includes determining one or more anomaly events coinciding with CM parameter changes based on the anomaly ratio; collating the PM, alarm, and CM data into a combined data set based on matching fields and timestamps; generating a set of rules linking one or more CM parameter changes and the collated data to anomaly events; and generating root cause explanations for CM parameter changes that are linked to anomaly events.