Machine Learning Model for EDFA Predicting SHB Effects
Publication Date: 9/22/2024
Event: 2024 European Conference on Optical Communication (ECOC)
Reference: pp. 1-4, 2024
Authors: Fatih Yaman, NEC Laboratories America, Inc.; Andrea D’Amico, NEC Laboratories America, Inc.; Eduardo Mateo, NEC Corporation; Takanori Inoue, NEC Corporation; Yoshihisa Inada, NEC Corporation
Abstract: Experiments show that the machine learning model of an EDFA is capable of modelling spectral hole burning effects accurately. As a result, it significantly outperforms black-box models that neglect inhomogeneous effects. The model achieves a record average RMSE of 0.0165 dB between the model predictions and measurements.
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