Bayesian Network Error Code Analytics for Telecom Root Cause

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Solution Overview

Problem

In telecommunications networks, identifying the root cause of failure conditions is challenging due to the complexity of error codes across different control plane protocols and network interfaces, requiring domain knowledge that may be outdated or incomplete.

Innovation Solution

The use of correlation coefficients and Bayesian networks to dynamically discover causation relationships among error codes, allowing for the identification of causation relationships between error codes agnostic to control plane protocols and network interfaces.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If domain knowledge is used to identify error codes and their relationships, then the analysis can be performed with existing expertise, but the domain knowledge may be outdated or incomplete, reducing reliability

Engineering Contradiction:

Improveaccuracy of root cause identificationVSAvoidcompleteness of domain knowledge

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The system performs self-learning by automatically analyzing error code data from the network to build and update the Bayesian network model without requiring manual domain knowledge updates. The system serves itself by continuously improving its own analytical capabilities through data-driven learning, eliminating the need for external domain expertise maintenance.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual domain knowledge-based analysis with an automated statistical modeling approach using Bayesian networks. This substitution transitions from a knowledge-dependent mechanical process to an data-driven automated system that objectively identifies causation relationships among error codes.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If traditional error code analysis methods are used, then the process is simple and requires minimal resources, but the troubleshooting efficiency is low and time-consuming

Engineering Contradiction:

Improvetroubleshooting efficiencyVSAvoidtime to identify root cause

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis by continuously collecting and storing error code data in a database, and pre-building the Bayesian network model structure. When a failure condition occurs, the pre-prepared model enables immediate root cause identification without requiring time-consuming manual analysis or data collection during the incident.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback by using the identified root cause information to continuously refine and update the Bayesian network model. The results of each analysis feed back into improving the model's accuracy, creating a continuous improvement cycle that enhances troubleshooting efficiency over time.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If comprehensive error code data from multiple control plane protocols and network interfaces is analyzed, then the coverage and accuracy improve, but the device complexity and computational requirements increase

Engineering Contradiction:

Improveaccuracy of error code analysisVSAvoidcomplexity of analysis system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The Bayesian network model serves as a universal framework that can handle error codes from multiple control plane protocols and network interfaces through a single unified analysis mechanism. This multi-functional approach eliminates the need for separate analysis systems for different protocols, reducing overall system complexity while maintaining comprehensive coverage.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system manages complexity by dynamically adjusting the parameters and structure of the Bayesian network based on the specific failure condition and available data. Rather than maintaining a fixed complex structure for all scenarios, the model adapts its parameters to match the actual problem being analyzed, reducing unnecessary computational overhead.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250112815A1Systems and methods for error code analytics in telecommunications networks

Publication Date: 2025.04.03 NETSCOUT SYSTEMS INC

AI summary

A system and method for analyzing error codes includes detecting a failure condition on a network, identifying a subset of subscribers impacted by the failure condition, determining for each subscriber in the subset of subscribers a first set of error codes associated with the failure condition, creating a Bayesian network comprising one or more error codes from the first set of error codes of each the subset of subscribers, computing a Conditional Probability Distribution (CPD) for each of the one or more error codes of the Bayesian network, and determining a second set of error codes based on the CPD, the second set of error codes indicative of a cause of the failure condition.