Data-driven modeling of erbium doped fiber amplifiers by neural networks

Abstract

Dependence of EDFA gain shape on input power and input spectrum shape is modelled using a simple neural network-based architecture for amplifiers with different gains and output powers. The model can predict the gain within ±0.1 dB. While the model has good success predicting the performance of an EDFA it is trained with, it is not as successful when predicting a different EDFA, or the same EDFA with different pump power. Retraining the model with a small amount of supplementary data from a separate EDFA makes the model able to predict the performance of the second EDFA with little loss in performance. Experiments show that machine learning model of an EDFA is capable of modelling spectralhole burning effects accurately. As a result, it significantly outperforms black-box models that neglect inhomogenous effects. Model achieves an average RMSE error of 0.016 dB between the model and measurements.

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