A Data-driven optimization of First-order Regular Perturbation Coefficients for Fiber Nonlinearities

Astrid Barreiro; Gabriele Liga; Alex Alvarado

doi:10.1109/IPC53466.2022.9975602

A Data-driven optimization of First-order Regular Perturbation Coefficients for Fiber Nonlinearities

Astrid Barreiro, Gabriele Liga, Alex Alvarado

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

3 Citaten (Scopus)

58 Downloads (Pure)

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We study the performance of gradient-descent optimization to estimate the coefficients of the discrete-time first-order regular perturbation (FRP). With respect to numerically computed coefficients, the optimized coefficients yield a model that (i) extends the FRP range of validity, and (ii) reduces the model’s complexity.

Originele taal-2	Engels
Titel	2022 IEEE Photonics Conference, IPC 2022 - Proceedings
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	1-2
Aantal pagina's	2
ISBN van elektronische versie	978-1-6654-3487-4
ISBN van geprinte versie	978-1-6654-3488-1
DOI's	https://doi.org/10.1109/IPC53466.2022.9975602
Status	Gepubliceerd - 14 dec. 2022
Evenement	2022 IEEE Photonics Conference, IPC 2022 - Vancouver, Canada Duur: 13 nov. 2022 → 17 nov. 2022

Congres

Congres	2022 IEEE Photonics Conference, IPC 2022
Land/Regio	Canada
Stad	Vancouver
Periode	13/11/22 → 17/11/22

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10.1109/IPC53466.2022.9975602

pdfDefinitieve gepubliceerde versie, 323 KB

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Fundamentals of the Nonlinear Optical Channel
Alvarado, A. (Project Manager), Liga, G. (Projectmedewerker), Barreiro, A. (Projectmedewerker), Willems, F. M. J. (Project communicatie medewerker), Sanders, R. (Project communicatie medewerker), Alvarado, A. (Project communicatie medewerker), Barreiro, A. (Project communicatie medewerker), Sheikh, A. (Projectmedewerker), Goossens, S. (Projectmedewerker), de Jonge, M. (Project communicatie medewerker), Gültekin, Y. C. (Projectmedewerker), Jaffal, Y. (Projectmedewerker), Oliari, V. (Projectmedewerker) & Ramachandran, V. (Projectmedewerker)
1/01/18 → 30/06/23
Project: Onderzoek direct

Citeer dit

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title = "A Data-driven optimization of First-order Regular Perturbation Coefficients for Fiber Nonlinearities",

abstract = "We study the performance of gradient-descent optimization to estimate the coefficients of the discrete-time first-order regular perturbation (FRP). With respect to numerically computed coefficients, the optimized coefficients yield a model that (i) extends the FRP range of validity, and (ii) reduces the model{\textquoteright}s complexity.",

keywords = "Perturbation methods, Computational modeling, Receivers, Numerical models, Complexity theory, Optimization, Photonics",

author = "Astrid Barreiro and Gabriele Liga and Alex Alvarado",

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doi = "10.1109/IPC53466.2022.9975602",

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booktitle = "2022 IEEE Photonics Conference, IPC 2022 - Proceedings",

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note = "2022 IEEE Photonics Conference, IPC 2022 ; Conference date: 13-11-2022 Through 17-11-2022",

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Barreiro, A , Liga, G & Alvarado, A 2022, A Data-driven optimization of First-order Regular Perturbation Coefficients for Fiber Nonlinearities. in 2022 IEEE Photonics Conference, IPC 2022 - Proceedings., 9975602, Institute of Electrical and Electronics Engineers, blz. 1-2, 2022 IEEE Photonics Conference, IPC 2022, Vancouver, British Columbia, Canada, 13/11/22. https://doi.org/10.1109/IPC53466.2022.9975602

A Data-driven optimization of First-order Regular Perturbation Coefficients for Fiber Nonlinearities. / Barreiro, Astrid ; Liga, Gabriele ; Alvarado, Alex.
2022 IEEE Photonics Conference, IPC 2022 - Proceedings. Institute of Electrical and Electronics Engineers, 2022. blz. 1-2 9975602.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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PY - 2022/12/14

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AB - We study the performance of gradient-descent optimization to estimate the coefficients of the discrete-time first-order regular perturbation (FRP). With respect to numerically computed coefficients, the optimized coefficients yield a model that (i) extends the FRP range of validity, and (ii) reduces the model’s complexity.

KW - Perturbation methods

KW - Computational modeling

KW - Receivers

KW - Numerical models

KW - Complexity theory

KW - Optimization

KW - Photonics

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DO - 10.1109/IPC53466.2022.9975602

M3 - Conference contribution

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BT - 2022 IEEE Photonics Conference, IPC 2022 - Proceedings

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A Data-driven optimization of First-order Regular Perturbation Coefficients for Fiber Nonlinearities

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Fundamentals of the Nonlinear Optical Channel

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