Machine learning techniques applied to system characterization and equalization

Darko Zibar; Jakob Thrane; Jesper Wass; Rasmus Thomas Jones; Molly Piels; Christian Schaeffer

doi:10.1364/OFC.2016.Tu3K.1

Machine learning techniques applied to system characterization and equalization

Darko Zibar, Jakob Thrane, Jesper Wass, Rasmus Thomas Jones, Molly Piels, Christian Schaeffer

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

Abstract

Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals.

Original language	English
Title of host publication	Proceedings of 2016 Optical Fiber Communications Conference and Exhibition
Number of pages	3
Publisher	Optical Society of America (OSA)
Publication date	2016
ISBN (Print)	9781943580071
DOIs	https://doi.org/10.1364/OFC.2016.Tu3K.1
Publication status	Published - 2016
Event	2016 Optical Fiber Communication Conference and Exhibition - Anaheim Convention Center, Anaheim, United States Duration: 20 Mar 2016 → 24 Mar 2016

Conference

Conference	2016 Optical Fiber Communication Conference and Exhibition
Location	Anaheim Convention Center
Country/Territory	United States
City	Anaheim
Period	20/03/2016 → 24/03/2016

Bibliographical note

From the session: DSP for Coherent Systems (Tu3K)

Keywords

signal processing
equalisers
learning (artificial intelligence)
optical fibre communication
parameter extraction
system characterization
system equalization
linear signal processing algorithms
machine learning algorithms
nonlinear fibre channel
Optical noise
Signal to noise ratio
Modulation
Phase noise
Estimation
Nonlinear optics
Optical polarization
Optical communication
Communication channel equalisation and identification
Signal processing and detection
Knowledge engineering techniques
Digital signal processing

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@inproceedings{4ed6b2ecbd1a49da9bf9eedde99ee07c,

title = "Machine learning techniques applied to system characterization and equalization",

abstract = "Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals.",

keywords = "signal processing, equalisers, learning (artificial intelligence), optical fibre communication, parameter extraction, system characterization, system equalization, linear signal processing algorithms, machine learning algorithms, nonlinear fibre channel, Optical noise, Signal to noise ratio, Modulation, Phase noise, Estimation, Nonlinear optics, Optical polarization, Optical communication, Communication channel equalisation and identification, Signal processing and detection, Knowledge engineering techniques, Digital signal processing",

author = "Darko Zibar and Jakob Thrane and Jesper Wass and Jones, {Rasmus Thomas} and Molly Piels and Christian Schaeffer",

note = "From the session: DSP for Coherent Systems (Tu3K); 2016 Optical Fiber Communication Conference and Exhibition, 2016 OFC ; Conference date: 20-03-2016 Through 24-03-2016",

year = "2016",

doi = "10.1364/OFC.2016.Tu3K.1",

language = "English",

isbn = "9781943580071",

booktitle = "Proceedings of 2016 Optical Fiber Communications Conference and Exhibition",

publisher = "Optical Society of America (OSA)",

}

Zibar, D, Thrane, J, Wass, J, Jones, RT, Piels, M & Schaeffer, C 2016, Machine learning techniques applied to system characterization and equalization. in Proceedings of 2016 Optical Fiber Communications Conference and Exhibition. Optical Society of America (OSA), 2016 Optical Fiber Communication Conference and Exhibition, Anaheim, California, United States, 20/03/2016. https://doi.org/10.1364/OFC.2016.Tu3K.1

TY - GEN

T1 - Machine learning techniques applied to system characterization and equalization

AU - Zibar, Darko

AU - Thrane, Jakob

AU - Wass, Jesper

AU - Jones, Rasmus Thomas

AU - Piels, Molly

AU - Schaeffer, Christian

N1 - From the session: DSP for Coherent Systems (Tu3K)

PY - 2016

Y1 - 2016

N2 - Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals.

AB - Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals.

KW - signal processing

KW - equalisers

KW - learning (artificial intelligence)

KW - optical fibre communication

KW - parameter extraction

KW - system characterization

KW - system equalization

KW - linear signal processing algorithms

KW - machine learning algorithms

KW - nonlinear fibre channel

KW - Optical noise

KW - Signal to noise ratio

KW - Modulation

KW - Phase noise

KW - Estimation

KW - Nonlinear optics

KW - Optical polarization

KW - Optical communication

KW - Communication channel equalisation and identification

KW - Signal processing and detection

KW - Knowledge engineering techniques

KW - Digital signal processing

U2 - 10.1364/OFC.2016.Tu3K.1

DO - 10.1364/OFC.2016.Tu3K.1

M3 - Article in proceedings

SN - 9781943580071

BT - Proceedings of 2016 Optical Fiber Communications Conference and Exhibition

PB - Optical Society of America (OSA)

T2 - 2016 Optical Fiber Communication Conference and Exhibition

Y2 - 20 March 2016 through 24 March 2016

ER -

Machine learning techniques applied to system characterization and equalization

Abstract

Conference

Bibliographical note

Keywords

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