Recurrent neural network for pre-distortion of combined nonlinear optical transmitter impairments with memory

Gil Paryanti; Lior Rokach; Hananel Faig; Dan Sadot

doi:10.1364/SPPCom.2018.SpW4G.5

Recurrent neural network for pre-distortion of combined nonlinear optical transmitter impairments with memory

Gil Paryanti, Lior Rokach, Hananel Faig, Dan Sadot

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

2 Scopus citations

Abstract

Pre-distortion for an optical transmitter modeled with complex frequency selective nonlinearity applicable to different system architecture, based on recurrent neural network is proposed. Several architectures are compared and above 20dB performance gain is presented.

Original language	English
Title of host publication	Signal Processing in Photonic Communications, SPPCom 2018
Publisher	Optica Publishing Group (formerly OSA)
ISBN (Print)	9781943580439
DOIs	https://doi.org/10.1364/SPPCom.2018.SpW4G.5
State	Published - 1 Jan 2018
Event	Signal Processing in Photonic Communications, SPPCom 2018 - Zurich, Switzerland Duration: 2 Jul 2018 → 5 Jul 2018

Publication series

Name	Optics InfoBase Conference Papers
Volume	Part F112-SPPCom 2018
ISSN (Electronic)	2162-2701

Conference

Conference	Signal Processing in Photonic Communications, SPPCom 2018
Country/Territory	Switzerland
City	Zurich
Period	2/07/18 → 5/07/18

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

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10.1364/SPPCom.2018.SpW4G.5

Cite this

Paryanti, G., Rokach, L., Faig, H., & Sadot, D. (2018). Recurrent neural network for pre-distortion of combined nonlinear optical transmitter impairments with memory. In Signal Processing in Photonic Communications, SPPCom 2018 (Optics InfoBase Conference Papers; Vol. Part F112-SPPCom 2018). Optica Publishing Group (formerly OSA). https://doi.org/10.1364/SPPCom.2018.SpW4G.5

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title = "Recurrent neural network for pre-distortion of combined nonlinear optical transmitter impairments with memory",

abstract = "Pre-distortion for an optical transmitter modeled with complex frequency selective nonlinearity applicable to different system architecture, based on recurrent neural network is proposed. Several architectures are compared and above 20dB performance gain is presented.",

author = "Gil Paryanti and Lior Rokach and Hananel Faig and Dan Sadot",

note = "Publisher Copyright: {\textcopyright} 2018 The Author(s).; Signal Processing in Photonic Communications, SPPCom 2018 ; Conference date: 02-07-2018 Through 05-07-2018",

year = "2018",

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doi = "10.1364/SPPCom.2018.SpW4G.5",

language = "English",

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series = "Optics InfoBase Conference Papers",

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Paryanti, G, Rokach, L, Faig, H & Sadot, D 2018, Recurrent neural network for pre-distortion of combined nonlinear optical transmitter impairments with memory. in Signal Processing in Photonic Communications, SPPCom 2018. Optics InfoBase Conference Papers, vol. Part F112-SPPCom 2018, Optica Publishing Group (formerly OSA), Signal Processing in Photonic Communications, SPPCom 2018, Zurich, Switzerland, 2/07/18. https://doi.org/10.1364/SPPCom.2018.SpW4G.5

Recurrent neural network for pre-distortion of combined nonlinear optical transmitter impairments with memory. / Paryanti, Gil; Rokach, Lior; Faig, Hananel et al.
Signal Processing in Photonic Communications, SPPCom 2018. Optica Publishing Group (formerly OSA), 2018. (Optics InfoBase Conference Papers; Vol. Part F112-SPPCom 2018).

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

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AU - Paryanti, Gil

AU - Rokach, Lior

AU - Faig, Hananel

AU - Sadot, Dan

PY - 2018/1/1

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N2 - Pre-distortion for an optical transmitter modeled with complex frequency selective nonlinearity applicable to different system architecture, based on recurrent neural network is proposed. Several architectures are compared and above 20dB performance gain is presented.

AB - Pre-distortion for an optical transmitter modeled with complex frequency selective nonlinearity applicable to different system architecture, based on recurrent neural network is proposed. Several architectures are compared and above 20dB performance gain is presented.

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DO - 10.1364/SPPCom.2018.SpW4G.5

M3 - Conference contribution

AN - SCOPUS:85051258142

SN - 9781943580439

T3 - Optics InfoBase Conference Papers

BT - Signal Processing in Photonic Communications, SPPCom 2018

PB - Optica Publishing Group (formerly OSA)

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Y2 - 2 July 2018 through 5 July 2018

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Recurrent neural network for pre-distortion of combined nonlinear optical transmitter impairments with memory

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