Classification Tree Localization of Anomaly-Linked Telecom Equipment
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Solution Overview
Problem
Existing systems face challenges in efficiently identifying and localizing faulty equipment in telecom networks, particularly in determining the specific regions within the network where anomalies occur.
Innovation Solution
A computer-implemented method using a classification tree-based machine learning model trained with network session reports to identify telecom network regions associated with anomalies, employing techniques such as cost-complexity pruning and recursive splitting to optimize the model for accurate anomaly localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional anomaly detection methods are used in telecom networks, then the system can detect anomalies, but the precision of localizing anomaly-associated equipment is insufficient
Solution Approach 1:
The patent introduces network session reports as an intermediary data structure that carries anomaly status information from network monitors to the classification system. These reports serve as a mediator between the monitoring infrastructure and the machine learning model, enabling precise anomaly localization without requiring direct complex interactions between all system components.
Solution Approach 2:
The patent replaces conventional mechanical or rule-based anomaly detection systems with a machine learning-based classification system. The classification tree model automatically learns patterns from training data to identify anomaly-associated equipment, substituting manual threshold-setting and rule-based detection with adaptive statistical learning.
2Measurement precision
If the classification tree model is trained with extensive network session reports, then the anomaly localization accuracy improves, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by collecting and storing network session reports in advance before actual anomaly detection is needed. The system maintains a database of historical session reports with anomaly status labels, which can be quickly loaded and used for training without time pressure during incident response. This pre-preparation of training data significantly reduces the time required when anomalies need to be detected.
3Measurement precision
If the classification tree model includes detailed network region hierarchies, then the localization specificity improves, but the model complexity and interpretation difficulty increase
Solution Approach 1:
The patent segments the telecom network into a hierarchical structure of network regions, where each region is represented by a unique identifier in the TNRH. This segmentation allows the classification tree to process complex network topologies by breaking them down into manageable hierarchical levels, improving both localization precision and model interpretability through structured data representation.
Data Source
AI summary
There is provided a processing circuitry-based method of localizing anomaly-associated equipment in a telecom network, comprising: a) receiving, from network monitors within the telecom network, network connectivity reports, wherein each report comprises: a respective telecom network region associated with a respective telecom network region hierarchy (TNRH), and a respective session anomaly status, b) training a classification tree-based machine learning model to classify a sequence telecom network region identifiers to an anomaly status, utilizing, a plurality of training tuples where each training tuple is based on a respective received network connectivity report; and c) identifying a telecom network region as including anomaly-associated equipment, based on identifying a decision path in the classification tree-based machine learning model, wherein a leaf of the identified decision path is associated with a given anomaly status.