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Nonlinearity compensation techniques using machine learning. / Sygletos, Stylianos; Redyuk, Alexey; Sidelnikov, Oleg.

Signal Processing in Photonic Communications, SPPCom 2019. OSA - The Optical Society, 2019. (Optics InfoBase Conference Papers; Vol. Part F137-SPPCom 2019).

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

Harvard

Sygletos, S, Redyuk, A & Sidelnikov, O 2019, Nonlinearity compensation techniques using machine learning. in Signal Processing in Photonic Communications, SPPCom 2019. Optics InfoBase Conference Papers, vol. Part F137-SPPCom 2019, OSA - The Optical Society, Signal Processing in Photonic Communications, SPPCom 2019, Burlingame, United States, 29.07.2019. https://doi.org/10.1364/SPPCOM.2019.SpT2E.2

APA

Sygletos, S., Redyuk, A., & Sidelnikov, O. (2019). Nonlinearity compensation techniques using machine learning. In Signal Processing in Photonic Communications, SPPCom 2019 (Optics InfoBase Conference Papers; Vol. Part F137-SPPCom 2019). OSA - The Optical Society. https://doi.org/10.1364/SPPCOM.2019.SpT2E.2

Vancouver

Sygletos S, Redyuk A, Sidelnikov O. Nonlinearity compensation techniques using machine learning. In Signal Processing in Photonic Communications, SPPCom 2019. OSA - The Optical Society. 2019. (Optics InfoBase Conference Papers). doi: 10.1364/SPPCOM.2019.SpT2E.2

Author

Sygletos, Stylianos ; Redyuk, Alexey ; Sidelnikov, Oleg. / Nonlinearity compensation techniques using machine learning. Signal Processing in Photonic Communications, SPPCom 2019. OSA - The Optical Society, 2019. (Optics InfoBase Conference Papers).

BibTeX

@inproceedings{8a93aa92e9ec449eb4fe3c7d17843467,
title = "Nonlinearity compensation techniques using machine learning",
abstract = "We discuss our recent work on machine learning based nonlinear equalization in long haul transmission sytems. We show that dynamic multi-perceptron networks can deal with the memory properties of the fibre c hannel and provide e fficient mitigation of nonlinear impairments at lower computational cost when compared to conventional digital back propagation methods.",
author = "Stylianos Sygletos and Alexey Redyuk and Oleg Sidelnikov",
year = "2019",
month = jan,
day = "1",
doi = "10.1364/SPPCOM.2019.SpT2E.2",
language = "English",
series = "Optics InfoBase Conference Papers",
publisher = "OSA - The Optical Society",
booktitle = "Signal Processing in Photonic Communications, SPPCom 2019",
note = "Signal Processing in Photonic Communications, SPPCom 2019 ; Conference date: 29-07-2019",

}

RIS

TY - GEN

T1 - Nonlinearity compensation techniques using machine learning

AU - Sygletos, Stylianos

AU - Redyuk, Alexey

AU - Sidelnikov, Oleg

PY - 2019/1/1

Y1 - 2019/1/1

N2 - We discuss our recent work on machine learning based nonlinear equalization in long haul transmission sytems. We show that dynamic multi-perceptron networks can deal with the memory properties of the fibre c hannel and provide e fficient mitigation of nonlinear impairments at lower computational cost when compared to conventional digital back propagation methods.

AB - We discuss our recent work on machine learning based nonlinear equalization in long haul transmission sytems. We show that dynamic multi-perceptron networks can deal with the memory properties of the fibre c hannel and provide e fficient mitigation of nonlinear impairments at lower computational cost when compared to conventional digital back propagation methods.

UR - http://www.scopus.com/inward/record.url?scp=85077209495&partnerID=8YFLogxK

U2 - 10.1364/SPPCOM.2019.SpT2E.2

DO - 10.1364/SPPCOM.2019.SpT2E.2

M3 - Conference contribution

AN - SCOPUS:85077209495

T3 - Optics InfoBase Conference Papers

BT - Signal Processing in Photonic Communications, SPPCom 2019

PB - OSA - The Optical Society

T2 - Signal Processing in Photonic Communications, SPPCom 2019

Y2 - 29 July 2019

ER -

ID: 22998599