Optical Link Cascaded Learning for Multi-Span OSNR and GSNR Prediction
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Solution Overview
Problem
Existing methods for predicting end-to-end optical link performance in multi-span optical systems face challenges in balancing measurement time and accuracy, with direct cascade methods accumulating errors and end-to-end methods requiring impractical link measurements.
Innovation Solution
A cascaded learning approach is applied using component optical amplifier, fiber, and transceiver models, trained separately and connected in the physical order of the optical link, with a small number of end-to-end measurements to adapt the model to the link.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If direct cascade of component-level models is used, then link measurement time is reduced, but model accuracy deteriorates due to accumulated errors
Solution Approach 1:
The optical link is segmented into multiple spans, each with its own component-level models (amplifier, fiber, transceiver). These segmented models are trained separately and then cascaded together, allowing the system to avoid full end-to-end measurements while maintaining accuracy through localized model training.
Solution Approach 2:
Component models for each span are trained in advance using measurement data before deployment. This preliminary training allows the models to be ready for rapid prediction without requiring extensive end-to-end measurements at deployment time, thus reducing measurement time while maintaining accuracy.
2Measurement precision
If end-to-end model is used, then model accuracy is improved, but link measurement requirements increase to impractical levels
Solution Approach 1:
Instead of treating the entire link as a single entity requiring comprehensive end-to-end measurements, the link is segmented into manageable spans. Each span is modeled separately with its own component models, dramatically reducing the total measurement requirements while maintaining prediction accuracy.
Solution Approach 2:
Component models are trained in advance using measurement data collected during system setup or maintenance. This preliminary training stores the measurement results in model parameters, so that future predictions can be made without repeating extensive measurements, thus reducing the quantity of measurements needed.
3Productivity
If component models are cascaded, then measurement time is reduced, but error accumulation increases
Solution Approach 1:
Each component model is trained in advance using actual measurement data from the specific hardware components. This preliminary training with real data ensures that the models capture the actual behavior of components, reducing errors when the models are cascaded for prediction.
Solution Approach 2:
The system uses a small number of end-to-end measurements to train the cascaded link model, providing feedback that adapts the component models to the specific physical link being modeled. This feedback mechanism corrects for any error accumulation and improves the reliability of predictions.
Data Source
AI summary
Disclosed is a method of cascaded learning applied to GSNR prediction using component optical amplifier, fiber, and transceiver models. The component models are measured and trained separately, before the devices are deployed into the field. Specifically, amplifier and transceiver model are trained based on measurement data, and fiber nonlinearity model are trained based on the synthesis data generated by a Gaussian Noise (GN) model. The optical link model contains all three component models and connects them as the physical order in the optical link. A small number of end-to-end measurements are used to train the optical link model to reduce the accumulated loss and adapt the model to the physical multi-span link.