Anomaly Detection for International Calling Performance Isolation
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Solution Overview
Problem
Troubleshooting international communication issues, such as international calls, is complicated due to the large number of destinations and carriers involved, making it difficult to determine and isolate performance degradation in a timely and efficient manner.
Innovation Solution
A method and system that utilize a ranking algorithm and anomaly detection model to categorize countries and key performance indicators (KPIs), compute percentage deviations, and apply weights to identify the root cause of performance degradation, providing engineers with a ranked list of issues to focus on for troubleshooting and network design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual troubleshooting methods are used for international calling issues, then detailed analysis can be performed, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing call detail records, identifying performance degradation patterns, and isolating root causes without requiring manual intervention. The anomaly detection model continuously monitors KPIs and autonomously generates troubleshooting recommendations, enabling the system to service itself and reduce manual troubleshooting time while maintaining high accuracy.
Solution Approach 2:
The patent replaces manual mechanical troubleshooting processes with automated computer-based anomaly detection and machine learning algorithms. The system uses computational models to analyze network performance data, identify anomalies, and generate diagnostic reports, substituting human analysts with automated digital systems that operate continuously without time loss.
2Reliability
If multiple carriers and destinations are monitored individually, then comprehensive coverage is achieved, but system complexity increases
Solution Approach 1:
The system merges monitoring of multiple carriers and destinations into a unified anomaly detection framework. By consolidating data from various sources into a single platform that uses common machine learning models and standardized KPIs, the system achieves comprehensive coverage while reducing operational complexity through centralization and standardization.
Solution Approach 2:
The anomaly detection model is designed with universal applicability across different carriers, countries, and network conditions. The same core system can monitor diverse international calling scenarios using a single unified architecture, making the system multi-functional and adaptable without requiring separate specialized systems for each carrier or destination.
3Measurement precision
If detailed analysis of all KPIs is performed, then comprehensive performance monitoring is achieved, but processing time increases
Solution Approach 1:
Instead of analyzing all possible KPIs equally, the system applies partial action by selectively focusing on the most critical and anomaly-prone KPIs based on historical data and machine learning predictions. The anomaly detection model identifies which KPIs are most likely to exhibit abnormal behavior and prioritizes those for detailed analysis, reducing processing time while maintaining comprehensive monitoring accuracy through intelligent selection rather than exhaustive analysis.
Data Source
AI summary
The disclosed system identifies international calling performance issues of a wireless telecommunication network. The system receives network traffic data for international calls including information about call attempts to a country. The system categorizes the country into a major category and a minor category based on the call attempts information. For a subset of countries, and for each key performance indicator in a subset of selected key performance indicators, the system monitors performance using an anomaly detection model to identify an anomaly in network performance, determines an actual value of the key performance indicator for the detected anomaly, and computes a variation value of the determined actual value based on a predicted range of values. The system ranks countries using the computed variation values, to indicate problematic parts of the wireless telecommunication network.