Automated Diagnosis Model Training via Network Simulation
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Solution Overview
Problem
Current mobile network outage management is inadequate due to the complexity and density of modern cellular networks, leading to inefficient and error-prone manual processes that cannot sustain future network demands, particularly in 5G and beyond scenarios.
Innovation Solution
An automated training manager system uses a simulated network to generate training data for machine learning-based diagnosis models, enabling efficient training without labeled datasets and allowing for self-healing networks to diagnose and mitigate faults without human expert assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used for network outage management, then human expertise can diagnose complex issues, but the process becomes inefficient and error-prone
Solution Approach 1:
The system enables self-service through automated diagnosis models that independently analyze network performance data, identify faults, and generate remediation actions without requiring continuous human intervention. The training manager automatically trains these models using simulated network data, making the system self-sufficient in handling routine diagnostic tasks.
Solution Approach 2:
Manual diagnostic processes are replaced with machine learning-based automated diagnosis models. These models substitute human expert analysis with algorithmic processing of network performance metrics, transforming qualitative human expertise into quantitative automated decision-making systems.
2Measurement precision
If automated diagnosis models are trained with labeled datasets, then training accuracy can be improved, but labeled datasets are scarce in mobile networks
Solution Approach 1:
The system creates synthetic copies of network data through simulation environments. The training manager uses network simulators to generate artificial network performance data that replicates real-world network behaviors and failure scenarios, providing abundant training data without requiring actual network outages or manual labeling.
Solution Approach 2:
The system performs preliminary data preparation by pre-generating synthetic training data through simulation before actual model training begins. This advance preparation creates a ready supply of labeled training examples that would otherwise require time-consuming manual collection and annotation from real network operations.
3Quantity of substance
If simulated networks are used to generate training data, then data availability increases, but the simulation must accurately replicate the target network
Solution Approach 1:
The training manager dynamically adjusts simulation parameters to match target network characteristics. By modifying simulation configuration parameters such as network topology, traffic patterns, and failure scenarios to reflect the actual network being diagnosed, the system ensures simulation outputs accurately represent real-world conditions while maintaining high data generation capacity.
Data Source
AI summary
A method and system for a training manager for generating diagnosis models for mobile networks. The method including selecting automatically a set of parameters for an action to be simulated in a simulated network where the simulated network replicates a target network for a diagnosis model, executing a simulation of an operation of a network based on the set of parameters of the action to generate an output of the simulation including a set of network performance metrics, transforming output of the simulation into training data for the diagnosis model, training the diagnosis model with the training data, and outputting the diagnosis model for the target network, in response to the diagnosis model meeting a designated quality threshold.