Wireless Network Root Cause Analysis for Failed UE Interactions
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Solution Overview
Problem
Troubleshooting failed interactions between mobile devices and wireless telecommunication networks is time-consuming due to the complexity of these systems, often requiring extensive log analysis and configuration inspections, leading to service degradation and delayed feature rollouts.
Innovation Solution
A system and method to determine the root cause of failed interactions by analyzing data from wireless telecommunication networks, utilizing a rules-based or machine learning model to identify missing connection summary parameters, signaling information, and RF measurements, and providing configuration diagnostics to identify configuration issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual troubleshooting methods are used to analyze log files and configuration parameters, then diagnostic accuracy can be maintained through expert inspection, but troubleshooting time increases significantly and service degradation worsens
Solution Approach 1:
The system enables self-service troubleshooting by automatically collecting data from multiple network elements, correlating logs, identifying root causes, and generating diagnostic reports without requiring manual expert intervention. The automated root cause analysis engine performs functions that previously required human analysts to inspect log files and configuration parameters manually.
Solution Approach 2:
The patent replaces the mechanical process of manual log analysis and configuration inspection with an automated computational system. The root cause analysis engine uses data processing algorithms to correlate information from multiple sources, substitute human expert inspection with machine-based automated diagnosis, and replace manual troubleshooting workflows with systematic automated analysis.
2Loss of information
If extensive log files from multiple network elements are collected for analysis, then diagnostic completeness is improved, but data processing complexity and time requirements increase
Solution Approach 1:
The system segments the complex troubleshooting task into distinct modular components: data collection from multiple network elements, data correlation, root cause analysis, and report generation. Each module handles specific aspects of the diagnostic process independently, making the overall complex system manageable and maintainable while ensuring comprehensive diagnostic coverage.
Solution Approach 2:
The patent introduces an intermediary root cause analysis engine that acts as a mediator between raw data from multiple network elements and the final diagnostic conclusions. This intermediary component correlates data from diverse sources, processes information systematically, and produces structured diagnostic reports, simplifying the complexity of handling extensive multi-source data.
3Measurement precision
If manual inspection of configuration parameters is performed, then accurate identification of configuration issues is achieved, but the process becomes time-consuming and delays service restoration
Solution Approach 1:
The system performs preliminary automated collection and preliminary analysis of configuration parameters from multiple network elements before full diagnostic processing. Configuration data is gathered in advance and pre-processed, allowing the root cause analysis engine to quickly identify configuration issues without requiring time-consuming manual inspection during the actual troubleshooting process.
Solution Approach 2:
The automated system performs self-service configuration analysis by automatically comparing configuration parameters against expected values, identifying mismatches, and flagging potential configuration issues without requiring manual expert review. This maintains accurate identification of configuration problems while dramatically reducing the time required compared to manual inspection methods.
Data Source
AI summary
The system obtains data associated with a failed interaction between a UE and a wireless telecommunication network and makes a series of determinations based on the data, including missing connection summary parameters, missing signaling information, missing component carrier parameters, whether the node in the network is properly configured, whether an RF measurement matches an expected RF measurement, and whether a condition associated with a channel used in the failed interaction is satisfactory. The system provides the results of the determinations to the root cause analytics engine, which in turn, based on the results, determines a root cause associated with the failed interaction.