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：我们提出了一种基于半监督、自归一化神经网络的新型机器学习框架，用于对多个EDFA的波长相关增益进行建模，从而实现一次性迁移学习。在Open Ireland和COSMOS测试平台上的22个EDFA实验中，即使在不同放大器类型之间进行操作，该框架仍展现出高精度的迁移学习性能。