We present a novel ML framework for modeling the wavelength-dependent gain of multiple EDFAs, based on semi-supervised, self-normalizing neural networks, enabling one-shot transfer learning. Our experiments on 22 EDFAs in Open Ireland and COSMOS testbeds show high-accuracy transfer-learning even when operated across different amplifier types.

翻译：我们提出了一种新颖的机器学习框架，用于对多个掺铒光纤放大器（EDFA）的波长相关增益进行建模。该框架基于半监督自归一化神经网络，支持一次性迁移学习。我们在Open Ireland和COSMOS测试平台上的22个EDFA上进行的实验表明，即使在不同放大器类型之间运行时，该框架也能实现高精度的迁移学习。