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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

53 Downloads (Pure)

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’s complexity.

Original language	English
Title of host publication	2022 IEEE Photonics Conference, IPC 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-2
Number of pages	2
ISBN (Electronic)	978-1-6654-3487-4
ISBN (Print)	978-1-6654-3488-1
DOIs	https://doi.org/10.1109/IPC53466.2022.9975602
Publication status	Published - 14 Dec 2022
Event	2022 IEEE Photonics Conference, IPC 2022 - Vancouver, Canada Duration: 13 Nov 2022 → 17 Nov 2022

Conference

Conference	2022 IEEE Photonics Conference, IPC 2022
Country/Territory	Canada
City	Vancouver
Period	13/11/22 → 17/11/22

Keywords

Perturbation methods
Computational modeling
Receivers
Numerical models
Complexity theory
Optimization
Photonics

Access to Document

10.1109/IPC53466.2022.9975602

pdfFinal published version, 323 KB

Cite this

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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",

year = "2022",

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doi = "10.1109/IPC53466.2022.9975602",

language = "English",

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pages = "1--2",

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

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

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, pp. 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. p. 1-2 9975602.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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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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

Abstract

Conference

Keywords

Access to Document

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FUN-NOTCH: Fundamentals of the Nonlinear Optical Channel

Cite this

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

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

FUN-NOTCH: Fundamentals of the Nonlinear Optical Channel

Cite this