Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices

Francesco Da Ros, Uiara Celine De Moura, Metodi P. Yankov

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


We report a neural-network based erbium-doped fiber amplifier (EDFA) gain model built from experimental measurements. The model shows low gain-prediction error for both the same device used for training (MSE leq 0.04 dB^2) and different physical units of the same make (generalization MSEleq 0.06 dB^2).

Original languageEnglish
Title of host publicationProceedings of 2020 European Conference on Optical Communications
Number of pages4
Publication dateDec 2020
Article number9333297
ISBN (Electronic)9781728173610
Publication statusPublished - Dec 2020
Event46th European Conference on Optical Communication - Virtual event, Brussels, Belgium
Duration: 6 Dec 202010 Dec 2020
Conference number: 46


Conference46th European Conference on Optical Communication
LocationVirtual event
Internet address

Bibliographical note

Funding Information:
This work is supported by the Villum Foundations (VYI grant OPTIC-AI no.29344), the EU H2020 programme (Marie Skłodowska-Curie grant no. 754462) and the DNRF CoE SPOC (ref. DNRF123).

Publisher Copyright:
© 2020 IEEE.


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