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

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

doi:10.1109/SUM48717.2021.9505741

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

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

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

We propose a Convolutional Neural Network (CNN) to learn the mapping between the 2D power profiles, (distance and frequency), and the Raman pumps. Using the CNN, the pump powers and wavelengths for arbitrary 2D profiles can be determined with high accuracy.

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 (Print)	978-1-6654-4673-0
DOIs	https://doi.org/10.1109/SUM48717.2021.9505741
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

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10.1109/SUM48717.2021.9505741

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@inproceedings{008f48ae6430457babcab119eca99478,

title = "Distance and spectral power profile shaping using machine learning enabled Raman amplifiers",

abstract = "We propose a Convolutional Neural Network (CNN) to learn the mapping between the 2D power profiles, (distance and frequency), and the Raman pumps. Using the CNN, the pump powers and wavelengths for arbitrary 2D profiles can be determined with high accuracy.",

author = "M. Soltani and {Da Ros}, F. and A. Carena and D. Zibar",

year = "2021",

doi = "10.1109/SUM48717.2021.9505741",

language = "English",

isbn = "978-1-6654-4673-0",

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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TY - GEN

T1 - Distance and spectral power profile shaping using machine learning enabled Raman amplifiers

AU - Soltani, M.

AU - Da Ros, F.

AU - Carena, A.

AU - Zibar, D.

PY - 2021

Y1 - 2021

N2 - We propose a Convolutional Neural Network (CNN) to learn the mapping between the 2D power profiles, (distance and frequency), and the Raman pumps. Using the CNN, the pump powers and wavelengths for arbitrary 2D profiles can be determined with high accuracy.

AB - We propose a Convolutional Neural Network (CNN) to learn the mapping between the 2D power profiles, (distance and frequency), and the Raman pumps. Using the CNN, the pump powers and wavelengths for arbitrary 2D profiles can be determined with high accuracy.

U2 - 10.1109/SUM48717.2021.9505741

DO - 10.1109/SUM48717.2021.9505741

M3 - Article in proceedings

SN - 978-1-6654-4673-0

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 -

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

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