AI Root Cause Analysis for 5G Network Anomaly Detection
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Solution Overview
Problem
Accurate detection of issues in complex 5G telecommunications networks is difficult due to multi-dimensional system anomalies, leading to processing delays, increased costs, and customer dissatisfaction.
Innovation Solution
Integrating time-series analysis and textual analysis with a pretrained sentiment analysis model to identify anomalies, root causes, and corrective actions in 5G environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring mechanisms are used in complex 5G networks, then system coverage is maintained, but detection accuracy of issues deteriorates due to multi-dimensional system anomalies
Solution Approach 1:
The patent segments the complex monitoring task into multiple specialized components: a time-series analysis module for quantitative temporal patterns, a sentiment analysis module for qualitative log assessment, and an integration module that combines both perspectives. This segmentation allows each module to specialize in specific aspects of anomaly detection, improving overall detection accuracy while managing system complexity through modular design
Solution Approach 2:
The patent introduces a dual-dimensional analysis approach by combining quantitative time-series analysis with qualitative sentiment analysis. This adds another dimension to traditional monitoring by simultaneously evaluating both numerical metric trends and textual log sentiments, enabling more accurate detection of multi-dimensional system anomalies in 5G networks
2Reliability
If comprehensive monitoring of thousands or millions of nodes is implemented, then network health coverage is improved, but processing time increases leading to delays
Solution Approach 1:
The patent divides the monitoring of thousands or millions of nodes into parallel processing streams handled by separate time-series analysis and sentiment analysis modules. This segmentation enables simultaneous processing of different data aspects across multiple nodes, maintaining comprehensive network health coverage while reducing overall processing time through parallel execution
Solution Approach 2:
The patent implements preliminary action by continuously pre-processing and analyzing time-series data and log data in real-time before critical anomalies develop. The system maintains running analyses of network metrics and sentiments, so when issues arise, detection occurs rapidly without requiring full re-analysis of historical data, thus reducing processing delays
3Measurement precision
If multiple data sources are integrated for analysis, then root cause identification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the integration of multiple data sources into distinct functional modules: time-series data processing, log data processing, and result integration. Each module handles specific data types and analysis tasks independently, improving root cause identification accuracy through comprehensive multi-source analysis while managing complexity through clear modular boundaries and specialized processing
Solution Approach 2:
The patent creates a universal integration framework that can handle multiple data sources (time-series metrics, log data, performance indicators) through a common architecture. The integration module serves multiple functions by combining results from different analysis types and presenting unified root cause identification, reducing overall system complexity through multi-functional design
Data Source
AI summary
Conditions are identified in a telecommunications network based on data collected from the telecommunications network. The data comprises time series telemetry data collected from telecommunications systems in the telecommunications network or live production data from the telecommunications network; and raw error logs collected alongside the time series telemetry data for the telecommunications systems. Outputs from a time series insight generator and a sentiment analyzer are combined to generate an output report indicative of anomalous metrics in the telecommunications network.