The gain spectrum of an Erbium-Doped Fiber Amplifier (EDFA) has a complex dependence on channel loading, pump power, and operating mode, making accurate modeling difficult to achieve. Machine Learning (ML) based modeling methods can achieve high accuracy, but they require comprehensive data collection. We present a novel ML-based Semi-Supervised, Self-Normalizing Neural Network (SS-NN) framework to model the wavelength dependent gain of EDFAs using minimal data, which achieve a Mean Absolute Error (MAE) of 0.07/0.08 dB for booster/pre-amplifier gain prediction. We further perform Transfer Learning (TL) using a single additional measurement per target-gain setting to transfer this model among 22 EDFAs in Open Ireland and COSMOS testbeds, which achieves a MAE of less than 0.19 dB even when operated across different amplifier types. We show that the SS-NN model achieves high accuracy for gain spectrum prediction with minimal data requirement when compared with current benchmark methods.

翻译：掺铒光纤放大器（EDFA）的增益谱对信道负载、泵浦功率和工作模式具有复杂的依赖关系，这使得精确建模难以实现。基于机器学习（ML）的建模方法可以实现高精度，但需要全面的数据采集。本文提出了一种新颖的基于ML的半监督自归一化神经网络（SS-NN）框架，利用最少的数据对EDFA的波长相关增益进行建模，该框架在功率放大器/前置放大器的增益预测中实现了0.07/0.08 dB的平均绝对误差（MAE）。我们进一步利用每个目标增益设置下的单次额外测量进行迁移学习（TL），将模型在Open Ireland和COSMOS测试平台的22个EDFA之间迁移，即使在不同类型的放大器上运行，其MAE也低于0.19 dB。研究表明，与当前基准方法相比，SS-NN模型在数据需求最小的情况下，能够实现增益谱预测的高精度。