An EDFA (Erbium-Doped Fiber Amplifier) is a type of optical amplifier used in fiber-optic communication systems to amplify optical signals. The core component of an EDFA is a segment of optical fiber that is doped with erbium ions. Erbium is chosen for its ability to amplify signals in the wavelength range around 1550 nanometers, which corresponds to the low-loss window of optical fibers.
The operation of an EDFA involves the process of stimulated emission, where the erbium ions, when excited by pumping laser light, release photons that are coherent with the incoming signal. This amplifies the optical signal passing through the doped fiber without the need for converting it into an electrical signal and then back into an optical signal.
We combine few-shot learning and grey-box modeling for EDFAs in optical lines, training a single EDFA model on 500 spectral loads and transferring it to other EDFAs using 4-8 samples, maintaining low OSNR prediction error.
A simple and accurate semi-analytical model for predicting the gain of a single-stage erbium-doped fiber amplifier embedded with an unknown gain flattening filter is proposed for precise system equalization that is crucial for submarine systems.
Experiments show that 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. Model achieves a record average RMSE of 0.0165 dB between the model predictions and measurements.
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. Even though the model has good success predicting the performance of the particular EDFA it is trained with, it is not as successful when used to predict a different EDFA, or even the same EDFA with a different pump power. However, retraining the model with a small amount of supplementary data from a second EDFA makes the model able to predict the performance of the second EDFA with little loss in performance.
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