Machine Learning for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

J. Wass; Jakob Thrane; Molly Piels; Júlio César Medeiros Diniz; Rasmus  Jones; Darko Zibar

Machine Learning for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

J. Wass
, Jakob Thrane
, Molly Piels
, Júlio César Medeiros Diniz
, Rasmus Jones
, Darko Zibar

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

Abstract

Supervised machine learning methods are applied and demonstrated experimentally for inband OSNR estimation and modulation format classification in optical communication systems. The proposed methods accurately evaluate coherent signals up to 64QAM using only intensity information.

Original language	English
Title of host publication	Proceedings of the 42nd European Conference and Exhibition on Optical Communications (ECOC 2016)
Number of pages	3
Publisher	VDE Verlag
Publication date	2016
Pages	1082-1084
ISBN (Electronic)	978-3-8007-4274-5
Publication status	Published - 2016
Event	42nd European Conference on Optical Communication - Dusseldorf, Germany Duration: 18 Sept 2016 → 22 Sept 2016

Conference

Conference	42nd European Conference on Optical Communication
Country/Territory	Germany
City	Dusseldorf
Period	18/09/2016 → 22/09/2016

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@inproceedings{089336ac537c4ae5b750aa9bc0c5d7ef,

title = "Machine Learning for Optical Performance Monitoring from Directly Detected PDM-QAM Signals",

abstract = "Supervised machine learning methods are applied and demonstrated experimentally for inband OSNR estimation and modulation format classification in optical communication systems. The proposed methods accurately evaluate coherent signals up to 64QAM using only intensity information.",

author = "J. Wass and Jakob Thrane and Molly Piels and \{Medeiros Diniz\}, \{J{\'u}lio C{\'e}sar\} and Rasmus Jones and Darko Zibar",

year = "2016",

language = "English",

pages = "1082--1084",

booktitle = "Proceedings of the 42nd European Conference and Exhibition on Optical Communications (ECOC 2016)",

publisher = "VDE Verlag",

note = "42nd European Conference on Optical Communication, ECOC 2016 ; Conference date: 18-09-2016 Through 22-09-2016",

}

Wass, J, Thrane, J, Piels, M, Medeiros Diniz, JC, Jones, R & Zibar, D 2016, Machine Learning for Optical Performance Monitoring from Directly Detected PDM-QAM Signals. in Proceedings of the 42nd European Conference and Exhibition on Optical Communications (ECOC 2016). VDE Verlag, pp. 1082-1084, 42nd European Conference on Optical Communication, Dusseldorf, Germany, 18/09/2016.

Machine Learning for Optical Performance Monitoring from Directly Detected PDM-QAM Signals. / Wass, J.; Thrane, Jakob; Piels, Molly et al.
Proceedings of the 42nd European Conference and Exhibition on Optical Communications (ECOC 2016). VDE Verlag, 2016. p. 1082-1084.

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

TY - GEN

T1 - Machine Learning for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

AU - Wass, J.

AU - Thrane, Jakob

AU - Piels, Molly

AU - Medeiros Diniz, Júlio César

AU - Jones, Rasmus

AU - Zibar, Darko

PY - 2016

Y1 - 2016

N2 - Supervised machine learning methods are applied and demonstrated experimentally for inband OSNR estimation and modulation format classification in optical communication systems. The proposed methods accurately evaluate coherent signals up to 64QAM using only intensity information.

AB - Supervised machine learning methods are applied and demonstrated experimentally for inband OSNR estimation and modulation format classification in optical communication systems. The proposed methods accurately evaluate coherent signals up to 64QAM using only intensity information.

M3 - Article in proceedings

SP - 1082

EP - 1084

BT - Proceedings of the 42nd European Conference and Exhibition on Optical Communications (ECOC 2016)

PB - VDE Verlag

T2 - 42nd European Conference on Optical Communication

Y2 - 18 September 2016 through 22 September 2016

ER -

Machine Learning for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

Abstract

Conference

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