A Novel Learned Volterra-based Scheme for Time-domain Nonlinear Equalization

Nelson Castro; Stylianos Sygletos

doi:10.1364/CLEO_SI.2022.SF3M.1

A Novel Learned Volterra-based Scheme for Time-domain Nonlinear Equalization

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

We introduce a learned time-domain Volterra-based equalizer to address nonlinear transmission impairments in fibre systems. When compared to conventional unoptimized equalization schemes it can offer more than 2.2 dB performance gain at reduced computational complexity.

Original language	English
Title of host publication	2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings
Subtitle of host publication	Science and Innovations, S and I 2022
Number of pages	2
ISBN (Electronic)	9781957171050
DOIs	https://doi.org/10.1364/CLEO_SI.2022.SF3M.1
Publication status	Published - 23 Sept 2022
Event	2022 Conference on Lasers and Electro-Optics, CLEO 2022 - San Jose, United States Duration: 15 May 2022 → 20 May 2022 https://www.cleoconference.org/home/schedule/

Publication series

Name	Optics InfoBase Conference Papers

Conference

Conference	2022 Conference on Lasers and Electro-Optics, CLEO 2022
Country/Territory	United States
City	San Jose
Period	15/05/22 → 20/05/22
Internet address	https://www.cleoconference.org/home/schedule/

Bibliographical note

Funding Information:
We introduced a time domain nonlinear equalization scheme based on a simplified IVSTF algorithm. Using ML that combines gradient back propagation and pruning it can result in more than 2.2 dB SNR improvement and a drastic reduction of its computational complexity compared to conventional NLEs. Acknowledgements: The work was supported by the EPSRC Programme Grant TRANSNET (EP/R035342/1).

Access to Document

10.1364/CLEO_SI.2022.SF3M.1

Cite this

@inproceedings{eef662caa8fb4b859385b0574b827e8c,

title = "A Novel Learned Volterra-based Scheme for Time-domain Nonlinear Equalization",

abstract = "We introduce a learned time-domain Volterra-based equalizer to address nonlinear transmission impairments in fibre systems. When compared to conventional unoptimized equalization schemes it can offer more than 2.2 dB performance gain at reduced computational complexity.",

author = "Nelson Castro and Stylianos Sygletos",

note = "Funding Information: We introduced a time domain nonlinear equalization scheme based on a simplified IVSTF algorithm. Using ML that combines gradient back propagation and pruning it can result in more than 2.2 dB SNR improvement and a drastic reduction of its computational complexity compared to conventional NLEs. Acknowledgements: The work was supported by the EPSRC Programme Grant TRANSNET (EP/R035342/1). ; 2022 Conference on Lasers and Electro-Optics, CLEO 2022 ; Conference date: 15-05-2022 Through 20-05-2022",

year = "2022",

month = sep,

day = "23",

doi = "10.1364/CLEO_SI.2022.SF3M.1",

language = "English",

series = "Optics InfoBase Conference Papers",

booktitle = "2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings",

url = "https://www.cleoconference.org/home/schedule/",

}

Castro, N & Sygletos, S 2022, A Novel Learned Volterra-based Scheme for Time-domain Nonlinear Equalization. in 2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings: Science and Innovations, S and I 2022., SF3M.1, Optics InfoBase Conference Papers, 2022 Conference on Lasers and Electro-Optics, CLEO 2022, San Jose, United States, 15/05/22. https://doi.org/10.1364/CLEO_SI.2022.SF3M.1

TY - GEN

T1 - A Novel Learned Volterra-based Scheme for Time-domain Nonlinear Equalization

AU - Castro, Nelson

AU - Sygletos, Stylianos

N1 - Funding Information: We introduced a time domain nonlinear equalization scheme based on a simplified IVSTF algorithm. Using ML that combines gradient back propagation and pruning it can result in more than 2.2 dB SNR improvement and a drastic reduction of its computational complexity compared to conventional NLEs. Acknowledgements: The work was supported by the EPSRC Programme Grant TRANSNET (EP/R035342/1).

PY - 2022/9/23

Y1 - 2022/9/23

N2 - We introduce a learned time-domain Volterra-based equalizer to address nonlinear transmission impairments in fibre systems. When compared to conventional unoptimized equalization schemes it can offer more than 2.2 dB performance gain at reduced computational complexity.

AB - We introduce a learned time-domain Volterra-based equalizer to address nonlinear transmission impairments in fibre systems. When compared to conventional unoptimized equalization schemes it can offer more than 2.2 dB performance gain at reduced computational complexity.

UR - http://www.scopus.com/inward/record.url?scp=85139969899&partnerID=8YFLogxK

UR - https://opg.optica.org/abstract.cfm?URI=CLEO_SI-2022-SF3M.1

UR - https://ieeexplore.ieee.org/document/9890493

U2 - 10.1364/CLEO_SI.2022.SF3M.1

DO - 10.1364/CLEO_SI.2022.SF3M.1

M3 - Conference publication

AN - SCOPUS:85139969899

T3 - Optics InfoBase Conference Papers

BT - 2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings

T2 - 2022 Conference on Lasers and Electro-Optics, CLEO 2022

Y2 - 15 May 2022 through 20 May 2022

ER -

A Novel Learned Volterra-based Scheme for Time-domain Nonlinear Equalization

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this