Forward Raman Amplifier Optimization Using Machine Learning-aided Physical Modeling

Metodi P. Yankov; Darko Zibar; Andrea Carena; Francesco Da Ros

doi:10.23919/OECC/PSC53152.2022.9849857

Forward Raman Amplifier Optimization Using Machine Learning-aided Physical Modeling

Metodi P. Yankov
, Darko Zibar
, Andrea Carena
, Francesco Da Ros

Polytechnic University of Turin

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

151 Downloads (Orbit)

Abstract

A differentiable nonlinear interpolation function learns the Raman gain efficiency and enables gradient-descent-based optimization of a Raman amplifier with arbitrary number of pumps. Example is given for unrepeatered links with a remote pumping stage.

Original language	English
Title of host publication	Proceedings of 27^th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing
Publisher	IEEE
Publication date	6 Jul 2022
Pages	1-3
Article number	9849857
ISBN (Print)	978-1-6654-8606-4
DOIs	https://doi.org/10.23919/OECC/PSC53152.2022.9849857
Publication status	Published - 6 Jul 2022
Event	27^th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC) - Toyama International Conference Center, Toyama, Japan Duration: 3 Jul 2022 → 6 Jul 2022 https://www.oeccpsc2022.org/about_oecc.html

Conference

Conference	27^th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC)
Location	Toyama International Conference Center
Country/Territory	Japan
City	Toyama
Period	03/07/2022 → 06/07/2022
Internet address	https://www.oeccpsc2022.org/about_oecc.html

Keywords

Interpolation
Stimulated emission
Computational modeling
Pumps
Training data
Optimization methods
Switches

Access to Document

10.23919/OECC/PSC53152.2022.9849857

FulltextAccepted author manuscript, 320 KB

OpenUrl availability

Check availability in DTU Findit

Cite this

Yankov, M. P., Zibar, D., Carena, A., & Ros, F. D. (2022). Forward Raman Amplifier Optimization Using Machine Learning-aided Physical Modeling. In Proceedings of 27^th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (pp. 1-3). Article 9849857 IEEE. https://doi.org/10.23919/OECC/PSC53152.2022.9849857

@inproceedings{0c1df004c07147f58de476ec0acd96f2,

title = "Forward Raman Amplifier Optimization Using Machine Learning-aided Physical Modeling",

abstract = "A differentiable nonlinear interpolation function learns the Raman gain efficiency and enables gradient-descent-based optimization of a Raman amplifier with arbitrary number of pumps. Example is given for unrepeatered links with a remote pumping stage.",

keywords = "Interpolation, Stimulated emission, Computational modeling, Pumps, Training data, Optimization methods, Switches",

author = "Yankov, \{Metodi P.\} and Darko Zibar and Andrea Carena and Ros, \{Francesco Da\}",

year = "2022",

month = jul,

day = "6",

doi = "10.23919/OECC/PSC53152.2022.9849857",

language = "English",

isbn = "978-1-6654-8606-4",

pages = "1--3",

booktitle = "Proceedings of 27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing",

publisher = "IEEE",

address = "United States",

note = "27<sup>th</sup> OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC), OECC/PSC 2022 ; Conference date: 03-07-2022 Through 06-07-2022",

url = "https://www.oeccpsc2022.org/about\_oecc.html",

}

Yankov, MP, Zibar, D, Carena, A & Ros, FD 2022, Forward Raman Amplifier Optimization Using Machine Learning-aided Physical Modeling. in Proceedings of 27^th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing., 9849857, IEEE, pp. 1-3, 27^th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC), Toyama, Japan, 03/07/2022. https://doi.org/10.23919/OECC/PSC53152.2022.9849857

Forward Raman Amplifier Optimization Using Machine Learning-aided Physical Modeling. / Yankov, Metodi P.; Zibar, Darko; Carena, Andrea et al.
Proceedings of 27^th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing. IEEE, 2022. p. 1-3 9849857.

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

TY - GEN

T1 - Forward Raman Amplifier Optimization Using Machine Learning-aided Physical Modeling

AU - Yankov, Metodi P.

AU - Zibar, Darko

AU - Carena, Andrea

AU - Ros, Francesco Da

PY - 2022/7/6

Y1 - 2022/7/6

N2 - A differentiable nonlinear interpolation function learns the Raman gain efficiency and enables gradient-descent-based optimization of a Raman amplifier with arbitrary number of pumps. Example is given for unrepeatered links with a remote pumping stage.

AB - A differentiable nonlinear interpolation function learns the Raman gain efficiency and enables gradient-descent-based optimization of a Raman amplifier with arbitrary number of pumps. Example is given for unrepeatered links with a remote pumping stage.

KW - Interpolation

KW - Stimulated emission

KW - Computational modeling

KW - Pumps

KW - Training data

KW - Optimization methods

KW - Switches

U2 - 10.23919/OECC/PSC53152.2022.9849857

DO - 10.23919/OECC/PSC53152.2022.9849857

M3 - Article in proceedings

SN - 978-1-6654-8606-4

SP - 1

EP - 3

BT - Proceedings of 27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing

PB - IEEE

T2 - 27<sup>th</sup> OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC)

Y2 - 3 July 2022 through 6 July 2022

ER -

Forward Raman Amplifier Optimization Using Machine Learning-aided Physical Modeling

Abstract

Conference

Keywords

Access to Document

OpenUrl availability

Fingerprint

Cite this