Machine Learning for DWDM Outage Prediction and Path Recommendation
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Solution Overview
Problem
DWDM networks experience outages due to various physical and optical issues, including fiber degradation, signal strength loss, and nonlinear effects, which current technologies struggle to predict and mitigate effectively.
Innovation Solution
A machine learning-based approach is employed to determine network topology, apply parameterized DWDM signals, and train models to predict path outages and recommend alternative paths by analyzing fiber network characteristics such as spectral efficiency and scattering effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained to predict DWDM path outages, then outage prediction accuracy improves, but computational resources and time required for training and prediction increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical outage data and network characteristics before actual outage prediction is needed. The models are trained in advance to recognize patterns and relationships between network parameters and outages, so that when actual prediction is required, the models can quickly provide accurate predictions without requiring real-time training. This pre-computation approach significantly reduces the time needed for actual outage prediction while maintaining high accuracy.
2Measurement precision
If machine learning models are trained to predict DWDM path outages, then outage prediction accuracy improves, but computational resources and complexity increase
Solution Approach 1:
The patent applies parameter changes by selecting and transforming network parameters into optimized feature representations that enhance model training efficiency and prediction accuracy. The system transforms raw network parameters (such as signal strength, wavelength, fiber characteristics) into processed features that capture essential outage patterns while reducing computational complexity. This parameter transformation allows the machine learning models to achieve high accuracy with more efficient computational requirements.
3Reliability
If alternative paths are recommended based on predicted outages, then network resilience improves, but system complexity and response time increase
Solution Approach 1:
The patent applies feedback by implementing a continuous loop where network outage predictions and alternative path recommendations are fed back into the network management system. When outages are predicted or detected, the system automatically provides alternative path recommendations, which are then monitored and fed back into the prediction models for continuous improvement. This feedback mechanism enhances network resilience by enabling adaptive response to changing network conditions while maintaining manageable system complexity through automated decision-making.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, predicting dense wavelength division multiplexing (DWDM) path outage prediction and recommendation of alternate communication paths. Parameterized DWDM signals are launched, and resulting network characteristics are determined. The parameters and characteristics are used to train one or more machine learning models subsequently used to predict outages and recommend alternate paths. Other embodiments are disclosed.