Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers

Autor: Uiara Celine de Moura, Andrea Carena, A. Margareth Rosa Brusin, Vittorio Curri, Darko Zibar

Publikováno v: Brusin, A M R, Moura, U C D, Curri, V, Zibar, D & Carena, A 2020, ' Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers ', Journal of Lightwave Technology, vol. 38, no. 23, pp. 6481-6491 . https://doi.org/10.1109/JLT.2020.3014810
Journal of Lightwave Technology

An ultra-fast machine learning based method for accurate predictions of gain and amplified spontaneous emission (ASE) noise profiles of Raman amplifiers is introduced. It is an alternative to high-complexity and time-consuming standard approaches, wh

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52123e24e1ea3bfe64334d776b0dee6b
https://doi.org/10.1109/jlt.2020.3014810

Neural network modeling of bismuth-doped fiber amplifier

Autor: Aleksandr Donodin, Uiara Celine de Moura, Ann Margareth Rosa Brusin, Egor Manuylovich, Vladislav Dvoyrin, Francesco Da Ros, Andrea Carena, Wladek Forysiak, Darko Zibar, Sergei K. Turitsyn

Publikováno v: Journal of the European Optical Society-Rapid Publications. 19:4

Bismuth-doped fiber amplifiers offer an attractive solution for meeting continuously growing enormous demand on the bandwidth of modern communication systems. However, practical deployment of such amplifiers require massive development and optimizati

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7cfaf6e4c43676ad10367d392b0b5bf8
https://doi.org/10.1051/jeos/2022016

Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning

Autor: Francesco Da Ros, Ann Margareth Rosa Brusin, Darko Zibar, Andrea Carena, Uiara Celine de Moura

Publikováno v: Optics Letters
de Moura, U C, Rosa Brusin, A M, Carena, A, Zibar, D & da Ros, F 2021, ' Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning ', Optics Letters, vol. 46, no. 5, pp. 1157-1160 . https://doi.org/10.1364/OL.417243

A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated. We employ a single-layer neural network to learn the mapping from the gain profiles to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3cab200525408f2d528c198e3262c010

Fiber-Agnostic Machine Learning-Based Raman Amplifier Models

Autor: de Moura, Uiara C., Zibar, Darko, Margareth Rosa Brusin, A., Carena, Andrea, Da Ros, Francesco

Publikováno v: Journal of Lightwave Technology; January 2023, Vol. 41 Issue: 1 p83-95, 13p

Experimental demonstration of arbitrary Raman gain–profile designs using machine learning

Autor: Darko Zibar, Uiara Celine de Moura, Andrea Carena, A. Margareth Rosa Brusin, Francesco Da Ros

Publikováno v: OFC
de Moura, U C, Da Ros, F, Brusin, A M R, Carena, A & Zibar, D 2020, Experimental demonstration of arbitrary Raman gain-profile designs using machine learning . in Optical Fiber Communication Conference 2020 ., T4B.2, IEEE, Optical Fiber Communication Conference 2020, San Diego, California, United States, 08/03/2020 . https://doi.org/10.1364/OFC.2020.T4B.2
Optical Fiber Communication Conference (OFC) 2020

A machine learning framework for Raman amplifier design is experimentally tested. Performance in terms of maximum error over the gain profile is investigated for various fiber types and lengths, demonstrating highly–accurate designs.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9177338e9432a5e8ee8ee86fda225ccc
https://doi.org/10.1364/ofc.2020.t4b.2