ML Root Cause Analysis for Wireless Network Test Failures
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Solution Overview
Problem
Conventional network testing solutions for cellular networks, such as 5G NR, are inadequate due to reliance on manual operations, which are time-consuming and prone to errors, and lack real-time updates and automatic configuration adjustments, leading to reduced interoperability, capacity, and security.
Innovation Solution
A network testing system utilizing machine learning to automate testing processes, provide real-time updates, and recommend configuration changes based on test failure analysis, thereby improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operations are used for network testing, then testing can be performed with simple tools, but the process is time-consuming and error-prone
Solution Approach 1:
The testing system performs self-diagnosis and self-configuration through machine learning models that automatically analyze test results, identify failures, and recommend configuration changes without requiring manual intervention for each testing step
Solution Approach 2:
Manual mechanical operations are replaced with automated machine learning-based systems that use algorithms to perform testing, analysis, and configuration tasks, substituting human operators with intelligent software agents
2Ease of operation
If manual testing processes are used, then system complexity is kept low, but real-time updates and automatic configuration adjustments are not available
Solution Approach 1:
The system implements continuous feedback loops where test results are automatically analyzed by machine learning models, which then generate real-time updates and configuration recommendations that are fed back into the network testing process
Solution Approach 2:
The machine learning models are pre-trained with extensive network configuration data and failure patterns, enabling them to quickly analyze test results and provide configuration recommendations without requiring complex real-time decision-making processes
3Reliability
If conventional testing methods are used, then interoperability issues can be identified, but the testing process lacks speed and reliability
Solution Approach 1:
The system automatically performs comprehensive network testing and analysis without requiring extensive manual setup or intervention, enabling rapid and reliable testing through self-service automation
Solution Approach 2:
The machine learning models dynamically adjust testing parameters and configuration settings based on real-time analysis of test results, enabling the system to optimize testing efficiency and reliability by changing operational parameters adaptively
Data Source
AI summary
Implementations are described herein for network testing using machine learning. In some implementations, a testing system receives an indication of a test failure associated with a test of a wireless communication system. The testing system stores one or more packet capture files associated with the test failure. A machine learning model associated with the testing system performs a root cause analysis of the test failure using the one or more packet capture files. The testing system determines a configuration update for the wireless communication system based on a result of the root cause analysis. The testing system generates an output that indicates the configuration update.