Optimization of Raman amplifiers using machine learning

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

doi:10.1109/SUM48717.2021.9505708

Optimization of Raman amplifiers using machine learning

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

Centre of Excellence for Silicon Photonics for Optical Communications

Polytechnic University of Turin

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

It has been recently demonstrated that neural networks can learn the complex pump-signal relations in Raman amplifiers. Here we experimentally show how these neural network models are applied to provide highly-accurate Raman amplifier designs and flexible configuration for ultra-wideband optical communication systems.

Original language	English
Title of host publication	Proceedings of 2021 IEEE Photonics Society Summer Topicals Meeting Series
Number of pages	2
Publisher	IEEE
Publication date	2021
ISBN (Electronic)	978-1-6654-1600-9
DOIs	https://doi.org/10.1109/SUM48717.2021.9505708
Publication status	Published - 2021
Event	2021 IEEE Photonics Society Summer Topicals Meeting Series - Virtual Conference, Cabo San Lucas, Mexico Duration: 19 Jul 2021 → 21 Jul 2021

Conference

Conference	2021 IEEE Photonics Society Summer Topicals Meeting Series
Location	Virtual Conference
Country/Territory	Mexico
City	Cabo San Lucas
Period	19/07/2021 → 21/07/2021

Keywords

Optical communications
Machine learning
Inverse system design
Optimization

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10.1109/SUM48717.2021.9505708

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@inproceedings{a4813a7034e2415caf8b848c2b597aa4,

title = "Optimization of Raman amplifiers using machine learning",

abstract = "It has been recently demonstrated that neural networks can learn the complex pump-signal relations in Raman amplifiers. Here we experimentally show how these neural network models are applied to provide highly-accurate Raman amplifier designs and flexible configuration for ultra-wideband optical communication systems.",

keywords = "Optical communications, Machine learning, Inverse system design, Optimization",

author = "{de Moura}, {U. C.} and {Da Ros}, F. and D. Zibar and {Rosa Brusin}, {A. M.} and A. Carena",

year = "2021",

doi = "10.1109/SUM48717.2021.9505708",

language = "English",

booktitle = "Proceedings of 2021 IEEE Photonics Society Summer Topicals Meeting Series",

publisher = "IEEE",

address = "United States",

note = "2021 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2021 ; Conference date: 19-07-2021 Through 21-07-2021",

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T1 - Optimization of Raman amplifiers using machine learning

AU - de Moura, U. C.

AU - Da Ros, F.

AU - Zibar, D.

AU - Rosa Brusin, A. M.

AU - Carena, A.

PY - 2021

Y1 - 2021

N2 - It has been recently demonstrated that neural networks can learn the complex pump-signal relations in Raman amplifiers. Here we experimentally show how these neural network models are applied to provide highly-accurate Raman amplifier designs and flexible configuration for ultra-wideband optical communication systems.

AB - It has been recently demonstrated that neural networks can learn the complex pump-signal relations in Raman amplifiers. Here we experimentally show how these neural network models are applied to provide highly-accurate Raman amplifier designs and flexible configuration for ultra-wideband optical communication systems.

KW - Optical communications

KW - Machine learning

KW - Inverse system design

KW - Optimization

U2 - 10.1109/SUM48717.2021.9505708

DO - 10.1109/SUM48717.2021.9505708

M3 - Article in proceedings

BT - Proceedings of 2021 IEEE Photonics Society Summer Topicals Meeting Series

PB - IEEE

T2 - 2021 IEEE Photonics Society Summer Topicals Meeting Series

Y2 - 19 July 2021 through 21 July 2021

ER -

Optimization of Raman amplifiers using machine learning

Abstract

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Keywords

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