Revisiting multi-step nonlinearity compensation with machine learning

Christian Häger; Henry D. Pfister; Rick M.  Bütler; Gabriele Liga; Alex  Alvarado

doi:10.1049/cp.2019.0947

Revisiting multi-step nonlinearity compensation with machine learning

Christian Häger, Henry D. Pfister, Rick M. Bütler, Gabriele Liga, Alex Alvarado

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

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Abstract

For the efficient compensation of fiber nonlinearity, one of the guiding principles appears to be: fewer steps are better and more efficient. We challenge this assumption and show that carefully designed multi-step approaches can lead to better performance-complexity trade-offs than their few-step counterparts.

Original language	English
Title of host publication	45th European Conference on Optical Communication (ECOC 2019)
Publisher	Institution of Engineering and Technology
Number of pages	4
ISBN (Print)	978-1-83953-185-9
DOIs	https://doi.org/10.1049/cp.2019.0947
Publication status	Published - 30 Jun 2020
Event	45th European Conference on Optical Communication (ECOC 2019) - Dublin, Ireland Duration: 22 Sept 2019 → 26 Sept 2019 Conference number: 45 https://www.ecoc2019.org/

Publication series

Name	IET Conference Publications
Number	CP765

Conference

Conference	45th European Conference on Optical Communication (ECOC 2019)
Abbreviated title	ECOC 2019
Country/Territory	Ireland
City	Dublin
Period	22/09/19 → 26/09/19
Internet address	https://www.ecoc2019.org/

Keywords

Deep Learning
Low-Complexity Digital Back-Propagation
Machine Learning Based Dsp
Polarization Mode Dispersion
Subband Processing

Access to Document

10.1049/cp.2019.0947

arXiv versionFinal author version , 347 KB

https://arxiv.org/abs/1904.09807

Cite this

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title = "Revisiting multi-step nonlinearity compensation with machine learning",

abstract = "For the efficient compensation of fiber nonlinearity, one of the guiding principles appears to be: fewer steps are better and more efficient. We challenge this assumption and show that carefully designed multi-step approaches can lead to better performance-complexity trade-offs than their few-step counterparts.",

keywords = "Deep Learning, Low-Complexity Digital Back-Propagation, Machine Learning Based Dsp, Polarization Mode Dispersion, Subband Processing",

author = "Christian H{\"a}ger and Pfister, {Henry D.} and B{\"u}tler, {Rick M.} and Gabriele Liga and Alex Alvarado",

year = "2020",

month = jun,

day = "30",

doi = "10.1049/cp.2019.0947",

language = "English",

isbn = "978-1-83953-185-9",

series = "IET Conference Publications",

publisher = "Institution of Engineering and Technology",

number = "CP765",

booktitle = "45th European Conference on Optical Communication (ECOC 2019)",

address = "United Kingdom",

note = "45th European Conference on Optical Communication (ECOC 2019), ECOC 2019 ; Conference date: 22-09-2019 Through 26-09-2019",

url = "https://www.ecoc2019.org/",

}

Häger, C, Pfister, HD, Bütler, RM, Liga, G & Alvarado, A 2020, Revisiting multi-step nonlinearity compensation with machine learning. in 45th European Conference on Optical Communication (ECOC 2019)., 9125675, IET Conference Publications, no. CP765, Institution of Engineering and Technology, 45th European Conference on Optical Communication (ECOC 2019), Dublin, Ireland, 22/09/19. https://doi.org/10.1049/cp.2019.0947

Revisiting multi-step nonlinearity compensation with machine learning. / Häger, Christian; Pfister, Henry D.; Bütler, Rick M. et al.
45th European Conference on Optical Communication (ECOC 2019). Institution of Engineering and Technology, 2020. 9125675 (IET Conference Publications; No. CP765).

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

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PY - 2020/6/30

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KW - Deep Learning

KW - Low-Complexity Digital Back-Propagation

KW - Machine Learning Based Dsp

KW - Polarization Mode Dispersion

KW - Subband Processing

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M3 - Conference contribution

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PB - Institution of Engineering and Technology

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Y2 - 22 September 2019 through 26 September 2019

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Revisiting multi-step nonlinearity compensation with machine learning

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

FUN-NOTCH: Fundamentals of the Nonlinear Optical Channel

Cite this

Revisiting multi-step nonlinearity compensation with machine learning

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

FUN-NOTCH: Fundamentals of the Nonlinear Optical Channel

Cite this