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Title: Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain
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. ©2023 The Author(s)  more » « less
Award ID(s):
2029295
PAR ID:
10457175
Author(s) / Creator(s):
Date Published:
Journal Name:
in Proc. European Conference on Optical Communication (ECOC’23) (to appear
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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