AI Root Cause Analysis for Wireless Network Anomalies
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Diagnosing operational anomalies in wireless communication networks is a time-consuming and labor-intensive process that relies heavily on the expertise of experienced administrators, which is not easily transferable and can lead to inefficiencies and resource wastage due to the reliance on a few experts.
Innovation Solution
An AI-based system using a generative AI model trained on network operations data and contextual information to identify the root cause of anomalies, reducing the need for human expertise by automating the diagnostic process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experienced administrators manually diagnose network issues, then diagnostic accuracy is improved, but diagnostic time and labor intensity increase significantly
Solution Approach 1:
The patent creates a digital copy of expert diagnostic knowledge by training an AI model on historical network operations data, error codes, and diagnostic outcomes. This digital replica enables automated diagnosis that replicates expert accuracy without requiring physical presence of experienced administrators, thereby reducing diagnostic time while maintaining measurement precision.
Solution Approach 2:
The patent replaces the mechanical system of human expert analysis with an automated AI-based diagnostic system. The AI model processes network operations data, error codes, and contextual information through computational algorithms, substituting human cognitive processes with machine learning operations that operate faster and without fatigue, thus reducing diagnostic time while maintaining or improving accuracy.
2Reliability
If multiple network administrators with different expertise are coordinated to diagnose cascading failures, then comprehensive diagnosis is improved, but process complexity and coordination overhead increase
Solution Approach 1:
The patent creates a universal diagnostic system that can handle multiple types of network failures across different domains through a single AI model. The model is trained on diverse network operations data from various network functions and interfaces, enabling it to perform comprehensive diagnosis without requiring multiple specialized administrators, thus reducing coordination complexity while maintaining reliability.
Solution Approach 2:
The patent merges the diagnostic capabilities of multiple experts into a single integrated AI system. By consolidating knowledge from administrators with different network domain expertise into one trained model, the system eliminates the need for coordination between multiple individuals while maintaining comprehensive diagnostic coverage across all network functions and interfaces.
3Measurement precision
If large quantities of network operations data are captured for analysis, then root cause identification accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing network operations data during normal operations, structuring it in ways that facilitate efficient AI model processing during anomaly detection. This includes pre-tagging data with metadata, organizing by network function and interface, and preparing contextual information in advance, so that when an anomaly occurs, the AI can quickly process relevant data without extensive real-time processing overhead.
Data Source
AI summary
Technology is disclosed herein for diagnosing root causes of operational anomalies on wireless networks in various implementations. In one example, program instructions direct a computing apparatus to detect an operational anomaly in a wireless network based on error code information and capture network operations data and contextual information relating to the operational anomaly. The program instructions further direct the computing apparatus to prompt an AI model to identify a root cause of the operational anomaly based on the error code information, the network operations data, and the contextual information and to receive output from the AI model including a root cause analysis of the operational anomaly.