Joint estimation of IQ phase and gain imbalances using convolutional neural networks on eye diagrams

Stefano Savian; Ju´lio Ce´sar Medeiros Diniz; Pak Tao Lau; Faisal Nadeem Khan; Simone Gaiarin; Rasmus Jones; Darko Zibar

doi:10.1364/CLEO_SI.2018.STh1C.3

Joint estimation of IQ phase and gain imbalances using convolutional neural networks on eye diagrams

Stefano Savian
, Ju´lio Ce´sar Medeiros Diniz
, Pak Tao Lau
, Faisal Nadeem Khan
, Simone Gaiarin
, Rasmus Jones
, Darko Zibar

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

A machine learning-based low-cost monitoring technique for transmitter IQ phase and gain imbalances is proposed. Simulations with formats up to NRZ-64QAM (28 GBd) show 95%- confidence estimation within 1.5° for phase and 0.06 for gain imbalances.

Original language	English
Title of host publication	CLEO
Subtitle of host publication	Science and Innovations, CLEO_SI 2018
Publisher	OSA - The Optical Society
Volume	Part F94-CLEO_SI 2018
ISBN (Electronic)	9781557528209
DOIs	https://doi.org/10.1364/CLEO_SI.2018.STh1C.3
Publication status	Published - 1 Jan 2018
Event	CLEO: Science and Innovations, CLEO_SI 2018 - San Jose, United States Duration: 13 May 2018 → 18 May 2018

Conference

Conference	CLEO: Science and Innovations, CLEO_SI 2018
Country/Territory	United States
City	San Jose
Period	13/05/18 → 18/05/18

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

More information

10.1364/CLEO_SI.2018.STh1C.3

Cite this

@inproceedings{4967379a08c64f31927f6fe6e0cba2cc,

title = "Joint estimation of IQ phase and gain imbalances using convolutional neural networks on eye diagrams",

abstract = "A machine learning-based low-cost monitoring technique for transmitter IQ phase and gain imbalances is proposed. Simulations with formats up to NRZ-64QAM (28 GBd) show 95\%- confidence estimation within 1.5° for phase and 0.06 for gain imbalances.",

author = "Stefano Savian and \{Medeiros Diniz\}, \{Ju´lio Ce´sar\} and Lau, \{Pak Tao\} and Khan, \{Faisal Nadeem\} and Simone Gaiarin and Rasmus Jones and Darko Zibar",

year = "2018",

month = jan,

day = "1",

doi = "10.1364/CLEO\_SI.2018.STh1C.3",

language = "English",

volume = "Part F94-CLEO\_SI 2018",

booktitle = "CLEO",

publisher = "OSA - The Optical Society",

note = "CLEO: Science and Innovations, CLEO\_SI 2018 ; Conference date: 13-05-2018 Through 18-05-2018",

}

Savian, S, Medeiros Diniz, JC, Lau, PT, Khan, FN, Gaiarin, S, Jones, R & Zibar, D 2018, Joint estimation of IQ phase and gain imbalances using convolutional neural networks on eye diagrams. in CLEO: Science and Innovations, CLEO_SI 2018. vol. Part F94-CLEO_SI 2018, OSA - The Optical Society, CLEO: Science and Innovations, CLEO_SI 2018, San Jose, United States, 13/05/18. https://doi.org/10.1364/CLEO_SI.2018.STh1C.3

Joint estimation of IQ phase and gain imbalances using convolutional neural networks on eye diagrams. / Savian, Stefano; Medeiros Diniz, Ju´lio Ce´sar; Lau, Pak Tao et al.
CLEO: Science and Innovations, CLEO_SI 2018. Vol. Part F94-CLEO_SI 2018 OSA - The Optical Society, 2018.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

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AU - Savian, Stefano

AU - Medeiros Diniz, Ju´lio Ce´sar

AU - Lau, Pak Tao

AU - Khan, Faisal Nadeem

AU - Gaiarin, Simone

AU - Jones, Rasmus

AU - Zibar, Darko

PY - 2018/1/1

Y1 - 2018/1/1

N2 - A machine learning-based low-cost monitoring technique for transmitter IQ phase and gain imbalances is proposed. Simulations with formats up to NRZ-64QAM (28 GBd) show 95%- confidence estimation within 1.5° for phase and 0.06 for gain imbalances.

AB - A machine learning-based low-cost monitoring technique for transmitter IQ phase and gain imbalances is proposed. Simulations with formats up to NRZ-64QAM (28 GBd) show 95%- confidence estimation within 1.5° for phase and 0.06 for gain imbalances.

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U2 - 10.1364/CLEO_SI.2018.STh1C.3

DO - 10.1364/CLEO_SI.2018.STh1C.3

M3 - Conference article published in proceeding or book

VL - Part F94-CLEO_SI 2018

BT - CLEO

PB - OSA - The Optical Society

T2 - CLEO: Science and Innovations, CLEO_SI 2018

Y2 - 13 May 2018 through 18 May 2018

ER -

Joint estimation of IQ phase and gain imbalances using convolutional neural networks on eye diagrams

Abstract

Conference

ASJC Scopus subject areas

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