Multi-band programmable gain Raman amplifier for high-capacity optical networks

Machine Learning for Power Profiles Prediction in Presence of Inter-channel Stimulated Raman Scattering

Rosa Brusin, A. M.
Zefreh, M. Ranjbar
Poggiolini, P.
Piciaccia, S.
Forghieri, F.
Carena, A.

January 2021

Two artificial neural network (ANN) models are presented to predict power profiles over C+L–band in ...

Softwarized and Autonomous Raman Amplifiers in Multi-Band Open Optical Networks

Borraccini, Giacomo
Ferrari, Alessio
Straullu, Stefano
Nespola, Antonino
D'Amico, Andrea
Piciaccia, Stefano
Galimberti, Gabriele
Tanzi, Alberto
Turolla, Silvia
Curri, Vittorio

January 2020

Starting from the software-defined-network (SDN) concept, this work aims to describe a softwarized s...

C+L Band Gain Design in Few-mode Fibers Using Raman Amplification and Machine Learning

Marcon, Gianluca
Galtarossa, Andrea
Palmieri, Luca
Santagiustina, Marco

January 2020

A machine learning technique was recently proposed to optimize the gain of a multi-pump single-mode...

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

de Moura U. C.
Rosa Brusin A. M.
Carena A.
Zibar D.
da Ros F.

January 2021

A machine learning framework predicting pump powers and noise figure profile for a target distribute...

Optimization of Raman amplifiers using machine learning

De Moura U. C.
Da Ros F.
Zibar D.
Rosa Brusin A. M.
Carena A.

January 2021

It has been recently demonstrated that neural networks can learn the complex pump–signal relations i...

Multi-band programmable gain Raman amplifier

De Moura, Uiara Celine
Iqbal, Md Asif
Kamalian-kopae, Morteza
Krzczanowicz, Lukasz
Da Ros, Francesco
Rosa Brusin, Ann Margareth
Carena, Andrea
Forysiak, Wladek
Turitsyn, Sergei K.
Zibar, Darko

January 2021

Optical communication systems, operating in C-band, are reaching their theoretically achievable capa...

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

de Moura U. C.
Ros F. D.
Rosa Brusin A. M.
Carena A.
Zibar D.

January 2020

A machine learning framework for Raman amplifier design is experimentally tested. Performance in ter...

Machine learning enabled Raman amplifiers

Zibar, D
de Moura, UC
Rosa Brusin, AM
Carena, A
Da Ros, F

January 2021

Ultra-wideband (UWB) optical communication systems, envision to operate in O+E+S+C+l band, are a via...

Load aware Raman gain profile prediction in dynamic multi-band optical networks

Rosa Brusin A. M.
de Moura U. C.
D'Amico A.
Curri V.
Zibar D.
Carena A.

January 2020

We introduce a load aware machine learning method for prediction of Raman gain profiles. It enables ...

Experimental Characterization of Raman Amplifier Optimization through Inverse System Design

De Moura U. C.
Da Ros F.
Rosa Brusin A. M.
Carena A.
Zibar D.

January 2021

Optical communication systems are always evolving to support the need for ever-increasing transmissi...

Distance and spectral power profile shaping using machine learning enabled Raman amplifiers

Soltani M.
Da Ros F.
Carena A.
Zibar D.

January 2021

We propose a Convolutional Neural Network (CNN) to learn the mapping between the 2D power profiles, ...

Inverse System Design Using Machine Learning: The Raman Amplifier Case

Zibar D.
Rosa Brusin A. M.
De Moura U. C.
Da Ros F.
Curri V.
Carena A.

January 2020

A wide range of highly-relevant problems in programmable and integrated photonics, optical amplifica...

Machine learning-based Raman amplifier design

Zibar, D.
Ferrari, A.
Curri, V.
Carena, A.

January 2019

A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump po...

Machine Learning Assisted Inverse Design for Ultrafine, Dynamic and Arbitrary Gain Spectrum Shaping of Raman Amplification

Yuting Huang
Jiangbing Du
Yufeng Chen
Ke Xu
Zuyuan He

July 2021

Distributed Raman amplifier (DRA) has been widely studied in recent decades because of its low noise...

Experimental validation of machine-learning based spectral-spatial power evolution shaping using Raman amplifiers

Soltani, Mehran
Da Ros, Francesco
Carena, Andrea
Zibar, Darko

September 2022

We experimentally validate a real-time machine learning framework, capable of controlling the pump p...

Machine Learning for Power Profiles Prediction in Presence of Inter-channel Stimulated Raman Scattering

Rosa Brusin, A. M.
Zefreh, M. Ranjbar
Poggiolini, P.
Piciaccia, S.
Forghieri, F.
Carena, A.

January 2021

Two artificial neural network (ANN) models are presented to predict power profiles over C+L–band in ...

Softwarized and Autonomous Raman Amplifiers in Multi-Band Open Optical Networks

Borraccini, Giacomo
Ferrari, Alessio
Straullu, Stefano
Nespola, Antonino
D'Amico, Andrea
Piciaccia, Stefano
Galimberti, Gabriele
Tanzi, Alberto
Turolla, Silvia
Curri, Vittorio

January 2020

Starting from the software-defined-network (SDN) concept, this work aims to describe a softwarized s...

C+L Band Gain Design in Few-mode Fibers Using Raman Amplification and Machine Learning

Marcon, Gianluca
Galtarossa, Andrea
Palmieri, Luca
Santagiustina, Marco

January 2020

A machine learning technique was recently proposed to optimize the gain of a multi-pump single-mode...

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

de Moura U. C.
Rosa Brusin A. M.
Carena A.
Zibar D.
da Ros F.

January 2021

A machine learning framework predicting pump powers and noise figure profile for a target distribute...

Optimization of Raman amplifiers using machine learning

De Moura U. C.
Da Ros F.
Zibar D.
Rosa Brusin A. M.
Carena A.

January 2021

It has been recently demonstrated that neural networks can learn the complex pump–signal relations i...

Multi-band programmable gain Raman amplifier

De Moura, Uiara Celine
Iqbal, Md Asif
Kamalian-kopae, Morteza
Krzczanowicz, Lukasz
Da Ros, Francesco
Rosa Brusin, Ann Margareth
Carena, Andrea
Forysiak, Wladek
Turitsyn, Sergei K.
Zibar, Darko

January 2021

Optical communication systems, operating in C-band, are reaching their theoretically achievable capa...

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

de Moura U. C.
Ros F. D.
Rosa Brusin A. M.
Carena A.
Zibar D.

January 2020

A machine learning framework for Raman amplifier design is experimentally tested. Performance in ter...

Machine learning enabled Raman amplifiers

Zibar, D
de Moura, UC
Rosa Brusin, AM
Carena, A
Da Ros, F

January 2021

Ultra-wideband (UWB) optical communication systems, envision to operate in O+E+S+C+l band, are a via...

Load aware Raman gain profile prediction in dynamic multi-band optical networks

Rosa Brusin A. M.
de Moura U. C.
D'Amico A.
Curri V.
Zibar D.
Carena A.

January 2020

We introduce a load aware machine learning method for prediction of Raman gain profiles. It enables ...

Experimental Characterization of Raman Amplifier Optimization through Inverse System Design

De Moura U. C.
Da Ros F.
Rosa Brusin A. M.
Carena A.
Zibar D.

January 2021

Optical communication systems are always evolving to support the need for ever-increasing transmissi...

Distance and spectral power profile shaping using machine learning enabled Raman amplifiers

Soltani M.
Da Ros F.
Carena A.
Zibar D.

January 2021

We propose a Convolutional Neural Network (CNN) to learn the mapping between the 2D power profiles, ...

Inverse System Design Using Machine Learning: The Raman Amplifier Case

Zibar D.
Rosa Brusin A. M.
De Moura U. C.
Da Ros F.
Curri V.
Carena A.

January 2020

A wide range of highly-relevant problems in programmable and integrated photonics, optical amplifica...

Machine learning-based Raman amplifier design

Zibar, D.
Ferrari, A.
Curri, V.
Carena, A.

January 2019

A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump po...

Machine Learning Assisted Inverse Design for Ultrafine, Dynamic and Arbitrary Gain Spectrum Shaping of Raman Amplification

Yuting Huang
Jiangbing Du
Yufeng Chen
Ke Xu
Zuyuan He

July 2021

Distributed Raman amplifier (DRA) has been widely studied in recent decades because of its low noise...

Experimental validation of machine-learning based spectral-spatial power evolution shaping using Raman amplifiers

Soltani, Mehran
Da Ros, Francesco
Carena, Andrea
Zibar, Darko

September 2022

We experimentally validate a real-time machine learning framework, capable of controlling the pump p...

Machine Learning for Power Profiles Prediction in Presence of Inter-channel Stimulated Raman Scattering

Rosa Brusin, A. M.
Zefreh, M. Ranjbar
Poggiolini, P.
Piciaccia, S.
Forghieri, F.
Carena, A.

January 2021

Two artificial neural network (ANN) models are presented to predict power profiles over C+L–band in ...

Softwarized and Autonomous Raman Amplifiers in Multi-Band Open Optical Networks

Borraccini, Giacomo
Ferrari, Alessio
Straullu, Stefano
Nespola, Antonino
D'Amico, Andrea
Piciaccia, Stefano
Galimberti, Gabriele
Tanzi, Alberto
Turolla, Silvia
Curri, Vittorio

January 2020

Starting from the software-defined-network (SDN) concept, this work aims to describe a softwarized s...

C+L Band Gain Design in Few-mode Fibers Using Raman Amplification and Machine Learning

Marcon, Gianluca
Galtarossa, Andrea
Palmieri, Luca
Santagiustina, Marco

January 2020

A machine learning technique was recently proposed to optimize the gain of a multi-pump single-mode...

Multi-band programmable gain Raman amplifier for high-capacity optical networks

Abstract

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Multi-band programmable gain Raman amplifier for high-capacity optical networks

Abstract

Extracted data

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