Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain

Autor:	Raj, Agastya, Wang, Zehao, Slyne, Frank, Chen, Tingjun, Kilper, Dan, Ruffini, Marco
Rok vydání:	2023
Předmět:	Computer Science - Networking and Internet Architecture Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: This paper is a preprint of a paper submitted to ECOC 2023 and is subject to Institution of Engineering and Technology Copyright. If accepted, the copy of record will be available at IET Digital Library
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.02233 Zobrazit plný text záznamu View this record from Arxiv