Optical frequency comb noise characterization using machine learning

Giovanni Brajato; Lars Lundberg; Victor Torres-Company; Darko Zibar

doi:10.1049/cp.2019.0889

Optical frequency comb noise characterization using machine learning

Giovanni Brajato^*, Lars Lundberg, Victor Torres-Company, Darko Zibar

^*Corresponding author for this work

Chalmers University of Technology

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

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Abstract

A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a mean-square-error-sense, works at wide range of SNRs and offers more accurate noise estimation compared to conventional methods.

Original language	English
Title of host publication	Proceedings of 45th European Conference on Optical Communication
Publisher	Institution of Engineering and Technology
Publication date	2019
Pages	572-575
ISBN (Print)	9781839531859
DOIs	https://doi.org/10.1049/cp.2019.0889
Publication status	Published - 2019
Event	45th European Conference on Optical Communication - Royal Dublin Showground, Dublin, Ireland Duration: 22 Sept 2019 → 26 Sept 2019 Conference number: 45 http://www.ecoc2019.org

Conference

Conference	45th European Conference on Optical Communication
Number	45
Location	Royal Dublin Showground
Country/Territory	Ireland
City	Dublin
Period	22/09/2019 → 26/09/2019
Internet address	http://www.ecoc2019.org

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@inproceedings{420dec0b58904bd78820956a6964a697,

title = "Optical frequency comb noise characterization using machine learning",

abstract = "A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a mean-square-error-sense, works at wide range of SNRs and offers more accurate noise estimation compared to conventional methods.",

author = "Giovanni Brajato and Lars Lundberg and Victor Torres-Company and Darko Zibar",

year = "2019",

doi = "10.1049/cp.2019.0889",

language = "English",

isbn = "9781839531859",

pages = "572--575",

booktitle = "Proceedings of 45th European Conference on Optical Communication",

publisher = "Institution of Engineering and Technology",

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

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

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Brajato, G, Lundberg, L, Torres-Company, V & Zibar, D 2019, Optical frequency comb noise characterization using machine learning. in Proceedings of 45th European Conference on Optical Communication. Institution of Engineering and Technology, pp. 572-575, 45th European Conference on Optical Communication, Dublin, Ireland, 22/09/2019. https://doi.org/10.1049/cp.2019.0889

Optical frequency comb noise characterization using machine learning. / Brajato, Giovanni; Lundberg, Lars; Torres-Company, Victor et al.
Proceedings of 45th European Conference on Optical Communication. Institution of Engineering and Technology, 2019. p. 572-575.

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

TY - GEN

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AU - Lundberg, Lars

AU - Torres-Company, Victor

AU - Zibar, Darko

N1 - Conference code: 45

PY - 2019

Y1 - 2019

N2 - A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a mean-square-error-sense, works at wide range of SNRs and offers more accurate noise estimation compared to conventional methods.

AB - A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a mean-square-error-sense, works at wide range of SNRs and offers more accurate noise estimation compared to conventional methods.

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DO - 10.1049/cp.2019.0889

M3 - Article in proceedings

SN - 9781839531859

SP - 572

EP - 575

BT - Proceedings of 45th European Conference on Optical Communication

PB - Institution of Engineering and Technology

T2 - 45th European Conference on Optical Communication

Y2 - 22 September 2019 through 26 September 2019

ER -

Optical frequency comb noise characterization using machine learning

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