AR/VR Network Issue Resolution via AI and LLM
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Solution Overview
Problem
Wireless telecommunication networks face challenges in rapid issue resolution due to the scarcity of adequately supported operators and delays in SMEs comprehending root causes, leading to prolonged downtimes and negative impacts on customer satisfaction and financial performance.
Innovation Solution
Integration of a large language model (LLM) and generative artificial intelligence (GenAI) into the network to automate issue recognition, provide recommendations for resolution, and streamline the issue resolution process, including code generation, testing, and deployment, using augmented reality (AR) tools for remote support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SMEs manually comprehend root causes and formulate resolution steps, then solution accuracy is improved, but resolution time increases
Solution Approach 1:
An AI intermediary system is introduced between the network issue and the SME. The AI automatically analyzes network data, identifies root causes, and generates preliminary resolution steps, which the SME then reviews and approves. This intermediary handles the time-consuming analysis work while the SME provides the necessary accuracy judgment, thus resolving the contradiction between speed and accuracy.
Solution Approach 2:
The AI system performs preliminary analysis actions before SME involvement by automatically detecting network issues, analyzing logs, identifying root causes, and formulating resolution recommendations in advance. This preliminary work reduces the time SMEs need to spend on manual analysis while maintaining solution accuracy through SME review of the pre-prepared recommendations.
2Productivity
If more operators are deployed for issue resolution, then productivity is improved, but operational complexity increases
Solution Approach 1:
The system enables self-service automated issue resolution where the AI autonomously performs issue detection, analysis, and resolution deployment without requiring multiple human operators. The AI independently manages the entire workflow from problem identification to solution implementation, significantly improving productivity while reducing operational complexity by eliminating the need for coordinated human teams.
3Reliability
If manual intervention is used for issue resolution, then solution reliability is improved, but response time worsens
Solution Approach 1:
The AI acts as an intermediary that handles rapid automated analysis and resolution deployment, while human SMEs serve as intermediaries for oversight and approval. This layered intermediary structure enables fast automated response while maintaining reliability through human-in-the-loop validation, resolving the contradiction between speed and reliability.
Solution Approach 2:
The system implements feedback loops where the AI continuously monitors network performance, evaluates the effectiveness of deployed resolutions, and learns from outcomes to improve future actions. This feedback mechanism ensures reliable decision-making through data-driven validation while maintaining rapid response times through automated iterative improvement.
Data Source
AI summary
The system obtains an issue associated with a network and resolves the issue by suggesting a solution and receiving an approval from an AR/VR device. Based on the issue, the system creates a ticket describing the issue and obtains diagnostic information associated with the issue. Based on the diagnostic information, the system determines a fix for the issue and sends a first notification to the AR/VR device. The first notification includes a description of the issue, the fix for the issue, and a request to test the fix. Upon receiving an approval to test the fix, the system performs regression testing using the fix and determines whether the fix passed the regression testing. Upon determining that the fix passed the regression testing, the system sends a second notification to the AR/VR device requesting an approval to deploy the fix, and upon receiving the approval, the system deploys the fix.