RAN Root Cause Analysis Using Correlation and ML Ranking
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Solution Overview
Problem
In modern communication systems, the overwhelming amount of data makes it challenging for RF engineers to manually identify and address root causes of radio access network (RAN) issues, such as increased access failures, which hinders efficient problem-solving and continuous improvement.
Innovation Solution
A modular root cause analysis system that automatically identifies, ranks, and mitigates network issues using an aggregator, correlator, and machine learning processor to determine the root cause and execute corrective actions, enabling efficient collaboration among multiple developers and reducing manual investigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual investigation is used to identify root causes of network issues, then engineers can analyze data with understanding, but the process is time-consuming and inefficient given the overwhelming amount of data
Solution Approach 1:
The system segments the root cause analysis process into distinct automated modules: data collection from network elements, data correlation to identify patterns, anomaly detection using machine learning, and root cause determination. This segmentation allows each module to specialize in specific tasks, improving both speed and accuracy while reducing manual intervention time.
Solution Approach 2:
The patent introduces an automated analysis system as an intermediary between raw network data and engineer decision-making. This intermediary system processes overwhelming data volumes, correlates events, detects anomalies, and presents structured findings, thereby reducing the time engineers spend on manual investigation while maintaining accurate root cause identification.
2Productivity
If automated analysis is implemented to reduce manual investigation time, then processing speed increases, but system complexity increases
Solution Approach 1:
The automated system is divided into separate functional modules (data collection, correlation, anomaly detection, root cause determination) that can be developed, maintained, and scaled independently. This modular architecture manages complexity by organizing functions into discrete units while maintaining high productivity through automated processing.
Solution Approach 2:
The system employs universal components such as machine learning models that can detect various types of anomalies across different network conditions, and correlation engines that work with diverse data sources. This multi-functionality reduces overall system complexity by using generalized solutions rather than specialized components for each specific problem.
3Reliability
If comprehensive data collection is performed to ensure accurate analysis, then analysis completeness improves, but data volume becomes overwhelming and harder to process
Solution Approach 1:
The system extracts only the most relevant features and events from comprehensive network data using correlation algorithms and anomaly detection. Instead of processing all raw data, the system identifies and extracts significant patterns, maintaining analysis completeness while reducing the effective data volume that requires detailed processing.
Solution Approach 2:
The patent applies different processing qualities to different data elements based on their importance. Critical events receive detailed analysis while routine data receives streamlined processing. This local quality approach ensures comprehensive coverage of important aspects while efficiently managing overall data volume through differentiated processing strategies.
Data Source
AI summary
The claimed system and method describes a root cause analysis system for a radio access network. Some aspects include automatic identification of possible causes for network issues, their ranking, determination of the root (main) cause and execution of related best actions, alerts and reporting in order to automatically identify, mitigate or eliminate the problem.