Machine Learning Network Simulator for Undersea Fiber Systems
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Solution Overview
Problem
Current undersea fiber optic communication system simulators rely on static environment-based behavioral files and databases, limiting their ability to accurately model real-world systems and requiring manual intensive editing, which restricts their applicability and prevents the use of machine learning for more accurate simulations.
Innovation Solution
A machine learning-based simulator generation method that automatically generates simulators for network elements using performance, configuration, and alarm information, allowing for flexible and configurable simulation environments that can model various system topologies and deployments, with the simulator learning and improving accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static environment-based behavioral files and databases are used to simulate network elements, then the simulation system can be implemented with current software, but the simulation accuracy and system replication capability are limited
Solution Approach 1:
The patent replaces the traditional mechanical/file-based simulation system with an AI-based system. Specifically, machine learning models are trained on historical network element data to generate simulated behavioral responses, replacing the static environmental files and databases. This substitution enables dynamic, accurate simulation of network element behaviors without manual configuration of complex behavioral files.
2Adaptability or versatility
If manual intensive editing is performed to introduce new use cases in static simulation environments, then new scenarios can be added, but the productivity and ease of operation deteriorate
Solution Approach 1:
The AI-based simulation system performs self-service by automatically generating simulated network element responses and behaviors based on trained machine learning models. When new use cases or scenarios need to be introduced, the system leverages its learned patterns to autonomously generate appropriate behavioral simulations without requiring manual editing of configuration files or databases, thereby maintaining high adaptability while improving productivity.
3Reliability
If static behavioral files are used to model network elements, then the simulation can be implemented with existing software, but the ability to accurately model real-world systems is limited
Solution Approach 1:
The patent applies preliminary action by training machine learning models in advance using historical network element data before deployment. This pre-training phase enables the AI system to learn realistic network element behaviors, failure patterns, and operational characteristics. When the simulation is executed, the pre-trained models immediately provide accurate system replication without requiring complex manual configuration, thereby improving reliability while maintaining ease of implementation.
Data Source
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AI summary
Techniques to generate network simulation scenarios are described. In one embodiment, an apparatus may comprise a records component operative to receive an example network configuration record; receive an example network operation record; a machine learning management component operative to generate a network operation model using a machine learning component based on the example network configuration record as an example input and the example network operation record as an example output; and a system-test component operative to receive a system-test network configuration record; and generate a system-test network operation record based on the system-test network configuration record using the network operation model. Other embodiments are described and claimed.