Generative Adversarial Networks for Telecommunication Site Issue Prediction
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Solution Overview
Problem
Current mobile network maintenance processes are reactive and time-consuming, often resulting in delayed issue resolution, which can impact customer experience and revenue, due to the inability to predict and proactively address novel issues.
Innovation Solution
A system utilizing a generative adversarial network to generate realistic future problems and a virtual agent to find effective solutions, with a relational description learner creating generic problem models for automated issue resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional supervised learning is used to predict issue occurrences, then known issues can be predicted, but novel issues that have never happened before cannot be addressed
Solution Approach 1:
The system performs preliminary action by proactively generating synthetic representations of novel issues before they occur in the network. The generative model creates hypothetical problem scenarios based on historical data patterns, allowing the system to prepare responses in advance rather than reacting after issues manifest.
Solution Approach 2:
The system uses copying by creating synthetic copies of potential novel issues through the generative model. These synthetic issue representations mimic real problem patterns but represent unseen scenarios, enabling the system to train on and prepare for novel issues without requiring actual occurrences.
2Reliability
If reactive problem solving is used with domain expert analysis, then thorough issue resolution is achieved, but resolution time is too long impacting customer experience
Solution Approach 1:
The system prepares potential solutions in advance by training the virtual agent on synthetic issue representations before real problems occur. When actual issues manifest, the pre-trained agent can immediately apply learned solutions without requiring time-consuming expert analysis, thus maintaining resolution quality while dramatically reducing response time.
Solution Approach 2:
The system implements self-service by enabling the virtual agent to autonomously diagnose and resolve issues without human expert intervention. The agent learns from synthetic training data and independently applies solutions to real network issues, eliminating the need for manual domain expert analysis while maintaining effective resolution.
3Reliability
If the current maintenance pipeline with multiple stages is used, then comprehensive issue handling is achieved, but the process takes too long especially for non-straightforward problems
Solution Approach 1:
The system consolidates multiple maintenance pipeline stages into a single pre-trained virtual agent that performs detection, diagnosis, and solution generation simultaneously. By training the agent comprehensively on synthetic data covering various issue types, the system achieves complete issue handling in one automated process rather than through sequential manual stages.
Solution Approach 2:
The system merges multiple separate maintenance functions (issue detection, analysis, solution formulation, and implementation) into a single integrated virtual agent. This consolidation eliminates the need for separate manual stages while maintaining comprehensive issue handling capabilities, significantly improving resolution speed.
4Loss of time
If limited problem-solving time is imposed on site engineers, then resolution speed may improve, but solution quality deteriorates with short-sighted fixes
Solution Approach 1:
The system enables automated self-service through the virtual agent that independently performs comprehensive issue analysis and solution implementation without human intervention. This automation eliminates the time pressure on engineers while ensuring consistent, high-quality solutions based on trained patterns rather than rushed human judgment.
Solution Approach 2:
The system uses synthetic copies of issues and their corresponding correct solutions to train the virtual agent. By learning from these copied training examples, the agent internalizes proper solution patterns and applies them automatically to real issues, ensuring high solution quality without requiring engineer time investment.
Data Source
AI summary
Methods include training, using a generative adversarial network, a generator model using data noise that includes data corresponding to real problems of a telecommunication site, generating a generated problem that has not occurred at the telecommunication site and that has a non-zero probability of occurring at the site in the future, providing the generated problem to a virtual agent that is configured to generate a solution action to resolve the generated problem, evaluating the solution action relative to the generated problem to determine a performance value corresponding to the solution action, and responsive to the performance value being higher than other performance values corresponding to other solution actions for the generated problem, generating a generic problem model that corresponds to the generated problem and that is associated with the solution action.