Power Evolution Prediction and Optimization in a Multi-span System Based on Component-wise System Modeling
This work addresses power management in optical communication systems, which is incremental as it applies existing modeling techniques to a specific experimental setup.
The authors tackled the problem of predicting and optimizing power profiles in multi-span optical communication systems by using a differentiable model combining a machine learning-based EDFA gain model and a stimulated Raman scattering fiber model, achieving results on an experimental fully-loaded C-band system.
Cascades of a machine learning-based EDFA gain model trained on a single physical device and a fully differentiable stimulated Raman scattering fiber model are used to predict and optimize the power profile at the output of an experimental multi-span fully-loaded C-band optical communication system.