Telecommunication Network Root Cause Analysis Using Knowledge Models
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Solution Overview
Problem
Current telecommunication networks face inefficiencies in troubleshooting due to their complexity, requiring highly trained personnel to diagnose and resolve issues, which is costly in terms of time and resources.
Innovation Solution
A method and system for performing root cause analysis in telecommunication networks using a knowledge model to identify causes of failures and provide recommendations, incorporating performance and configuration data analysis across both core and radio access networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly trained personnel are used to diagnose and resolve network issues, then troubleshooting accuracy is improved, but operational costs and time consumption increase
Solution Approach 1:
The system performs preliminary triage and problem categorization automatically before human intervention is needed. Call center systems pre-analyze reported issues, categorize them by type and severity, and prepare initial diagnostic data, so that when technicians do intervene, the work is already partially completed and more efficient.
Solution Approach 2:
An automated knowledge base and diagnostic system acts as an intermediary between the complex network infrastructure and human technicians. This intermediary system processes raw network data, identifies potential issues, and presents structured diagnostic information to technicians, reducing the cognitive load and training requirements while maintaining high diagnostic accuracy.
2Reliability
If highly trained personnel are deployed for network troubleshooting, then problem resolution quality is improved, but resource requirements increase
Solution Approach 1:
The network system performs self-diagnosis and self-monitoring through automated agents that continuously collect performance data, detect anomalies, and generate diagnostic reports. This self-service capability reduces the need for human resources while maintaining high problem resolution quality, as the system can identify and often resolve issues without human intervention.
Solution Approach 2:
Manual troubleshooting processes are replaced with automated diagnostic systems that use algorithms and knowledge bases to analyze network data. This substitution of mechanical human analysis with automated computational systems reduces resource requirements while maintaining or improving resolution quality through consistent, data-driven diagnostics.
3Measurement precision
If comprehensive triage and analysis processes are implemented, then diagnostic accuracy is improved, but system complexity increases
Solution Approach 1:
The comprehensive diagnostic process is segmented into distinct modular components: data collection modules, analysis modules, knowledge base modules, and reporting modules. Each module handles a specific aspect of the diagnostic process, making the overall complex system manageable through clear separation of concerns and independent optimization of each segment.
Solution Approach 2:
The diagnostic system is designed with universal components that can handle multiple types of network issues and various network configurations through a single integrated platform. The knowledge base and analysis engines are configured to work across different network types and failure modes, reducing system complexity by avoiding the need for separate specialized systems for each diagnostic scenario.
Data Source
AI summary
A method, computer program product and system for performing performance and root cause analysis of failures in a telecommunication network are provided. The telecommunication network includes User Equipment (UE) devices, core network and radio access network (RAN). Information related to impacted performance and failures reported in the telecommunication network is received. Telecommunication and transport network elements associated with the reported network performance and failures are identified. Performance and configuration data associated with the identified network elements is analyzed to identify one or more causes of the reported network failures. A root cause analysis of the reported network failures is performed using knowledge and statistical inference models for each of the identified causes to provide at least one recommendation for resolving the reported network failures.