Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

Darko Zibar; Luis Henrique Hecker de Carvalho; Molly Piels; Andy Doberstein; Julio Diniz; Bernd Nebendahl; Carolina Franciscangelis; Jose Manuel Estaran Tolosa; Hansjoerg Haisch; Neil G. Gonzalez; Julio Cesar R. F. de Oliveira; Idelfonso  Tafur Monroy

doi:10.1109/JLT.2015.2394808

Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

Darko Zibar (Invited author), Luis Henrique Hecker de Carvalho (Invited author), Molly Piels (Invited author), Andy Doberstein (Invited author), Julio Diniz (Invited author), Bernd Nebendahl (Invited author), Carolina Franciscangelis (Invited author), Jose Manuel Estaran Tolosa (Invited author), Hansjoerg Haisch (Invited author), Neil G. Gonzalez (Invited author), Julio Cesar R. F. de Oliveira (Invited author), Idelfonso Tafur Monroy (Invited author)

Research output: Contribution to journal › Journal article › Research › peer-review

Abstract

In this paper, tools from machine learning community, such as Bayesian filtering and expectation maximization parameter estimation, are presented and employed for laser amplitude and phase noise characterization. We show that phase noise estimation based on Bayesian filtering outperforms conventional time-domain approach in the presence of moderate measurement noise. Additionally, carrier synchronization based on Bayesian filtering, in combination with expectation maximization, is demonstrated for the first time experimentally.

Original language	English
Journal	Journal of Lightwave Technology
Volume	33
Issue number	7
Pages (from-to)	1333-1343
ISSN	0733-8724
DOIs	https://doi.org/10.1109/JLT.2015.2394808
Publication status	Published - 2015

Keywords

Communication, Networking and Broadcast Technologies
Photonics and Electrooptics
Bayes methods
Bayesian filtering
Expectation maximization
Kalman filters
Mathematical model
Optical communication
Phase noise
State-space methods
Synchronization
Vectors

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10.1109/JLT.2015.2394808

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Zibar, D., de Carvalho, L. H. H., Piels, M., Doberstein, A., Diniz, J., Nebendahl, B., Franciscangelis, C., Estaran Tolosa, J. M., Haisch, H., Gonzalez, N. G., de Oliveira, J. C. R. F., & Tafur Monroy, I. (2015). Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization. Journal of Lightwave Technology, 33(7), 1333-1343. https://doi.org/10.1109/JLT.2015.2394808

@article{adbef512f41e45608113803cc11f458f,

title = "Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization",

abstract = "In this paper, tools from machine learning community, such as Bayesian filtering and expectation maximization parameter estimation, are presented and employed for laser amplitude and phase noise characterization. We show that phase noise estimation based on Bayesian filtering outperforms conventional time-domain approach in the presence of moderate measurement noise. Additionally, carrier synchronization based on Bayesian filtering, in combination with expectation maximization, is demonstrated for the first time experimentally.",

keywords = "Communication, Networking and Broadcast Technologies, Photonics and Electrooptics, Bayes methods, Bayesian filtering, Expectation maximization, Kalman filters, Mathematical model, Optical communication, Phase noise, State-space methods, Synchronization, Vectors",

author = "Darko Zibar and {de Carvalho}, {Luis Henrique Hecker} and Molly Piels and Andy Doberstein and Julio Diniz and Bernd Nebendahl and Carolina Franciscangelis and {Estaran Tolosa}, {Jose Manuel} and Hansjoerg Haisch and Gonzalez, {Neil G.} and {de Oliveira}, {Julio Cesar R. F.} and {Tafur Monroy}, Idelfonso",

year = "2015",

doi = "10.1109/JLT.2015.2394808",

language = "English",

volume = "33",

pages = "1333--1343",

journal = "Journal of Lightwave Technology",

issn = "0733-8724",

publisher = "Institute of Electrical and Electronics Engineers",

number = "7",

}

Zibar, D, de Carvalho, LHH, Piels, M, Doberstein, A, Diniz, J, Nebendahl, B, Franciscangelis, C, Estaran Tolosa, JM, Haisch, H, Gonzalez, NG, de Oliveira, JCRF & Tafur Monroy, I 2015, 'Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization', Journal of Lightwave Technology, vol. 33, no. 7, pp. 1333-1343. https://doi.org/10.1109/JLT.2015.2394808

Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization. / Zibar, Darko (Invited author); de Carvalho, Luis Henrique Hecker (Invited author); Piels, Molly (Invited author) et al.
In: Journal of Lightwave Technology, Vol. 33, No. 7, 2015, p. 1333-1343.

Research output: Contribution to journal › Journal article › Research › peer-review

TY - JOUR

T1 - Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

AU - Zibar, Darko

AU - de Carvalho, Luis Henrique Hecker

AU - Piels, Molly

AU - Doberstein, Andy

AU - Diniz, Julio

AU - Nebendahl, Bernd

AU - Franciscangelis, Carolina

AU - Estaran Tolosa, Jose Manuel

AU - Haisch, Hansjoerg

AU - Gonzalez, Neil G.

AU - de Oliveira, Julio Cesar R. F.

AU - Tafur Monroy, Idelfonso

PY - 2015

Y1 - 2015

N2 - In this paper, tools from machine learning community, such as Bayesian filtering and expectation maximization parameter estimation, are presented and employed for laser amplitude and phase noise characterization. We show that phase noise estimation based on Bayesian filtering outperforms conventional time-domain approach in the presence of moderate measurement noise. Additionally, carrier synchronization based on Bayesian filtering, in combination with expectation maximization, is demonstrated for the first time experimentally.

AB - In this paper, tools from machine learning community, such as Bayesian filtering and expectation maximization parameter estimation, are presented and employed for laser amplitude and phase noise characterization. We show that phase noise estimation based on Bayesian filtering outperforms conventional time-domain approach in the presence of moderate measurement noise. Additionally, carrier synchronization based on Bayesian filtering, in combination with expectation maximization, is demonstrated for the first time experimentally.

KW - Communication, Networking and Broadcast Technologies

KW - Photonics and Electrooptics

KW - Bayes methods

KW - Bayesian filtering

KW - Expectation maximization

KW - Kalman filters

KW - Mathematical model

KW - Optical communication

KW - Phase noise

KW - State-space methods

KW - Synchronization

KW - Vectors

U2 - 10.1109/JLT.2015.2394808

DO - 10.1109/JLT.2015.2394808

M3 - Journal article

SN - 0733-8724

VL - 33

SP - 1333

EP - 1343

JO - Journal of Lightwave Technology

JF - Journal of Lightwave Technology

IS - 7

ER -

Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

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Keywords

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