Numerical Analysis of Artificial Neural Network and Volterra-based Nonlinear Equalizers for Coherent Optical OFDM

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00230717" target="_blank" >RIV/68407700:21230/15:00230717 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1117/12.2079905" target="_blank" >http://dx.doi.org/10.1117/12.2079905</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.2079905" target="_blank" >10.1117/12.2079905</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Numerical Analysis of Artificial Neural Network and Volterra-based Nonlinear Equalizers for Coherent Optical OFDM

Popis výsledku v původním jazyce

One major drawback of coherent optical orthogonal frequency-division multiplexing (CO-OFDM) that hitherto remains unsolved is its vulnerability to nonlinear fiber effects due to its high peak-to-average power ratio. Several digital signal processing techniques have been investigated for the compensation of fiber nonlinearities, e.g. digital back-propagation, nonlinear pre- and post-compensation and nonlinear equalizers (NLEs) based on the inverse Volterra-series transfer function (IVSTF). Alternatively,nonlinearities can be mitigated using nonlinear decision classifiers such as artificial neural networks (ANNs) based on a multilayer perceptron. In this paper, ANN-NLE is presented for a 16 QAM CO-OFDM system. The capability of the proposed approach tocompensate the fiber nonlinearities is numerically demonstrated for up to 100-Gb/s over 1000 km and compared to the benchmark IVSTF-NLE. Results show that in terms of Q-factor, for 100-Gb/s at 1000 km of transmission, ANN-NLE outperforms

Název v anglickém jazyce

Numerical Analysis of Artificial Neural Network and Volterra-based Nonlinear Equalizers for Coherent Optical OFDM

Popis výsledku anglicky

One major drawback of coherent optical orthogonal frequency-division multiplexing (CO-OFDM) that hitherto remains unsolved is its vulnerability to nonlinear fiber effects due to its high peak-to-average power ratio. Several digital signal processing techniques have been investigated for the compensation of fiber nonlinearities, e.g. digital back-propagation, nonlinear pre- and post-compensation and nonlinear equalizers (NLEs) based on the inverse Volterra-series transfer function (IVSTF). Alternatively,nonlinearities can be mitigated using nonlinear decision classifiers such as artificial neural networks (ANNs) based on a multilayer perceptron. In this paper, ANN-NLE is presented for a 16 QAM CO-OFDM system. The capability of the proposed approach tocompensate the fiber nonlinearities is numerically demonstrated for up to 100-Gb/s over 1000 km and compared to the benchmark IVSTF-NLE. Results show that in terms of Q-factor, for 100-Gb/s at 1000 km of transmission, ANN-NLE outperforms

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of PIERS 2015 in Prague

ISBN

978-1-934142-30-1

ISSN

1559-9450

e-ISSN

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Počet stran výsledku

5

Strana od-do

2473-2477

Název nakladatele

Electromagnetics Academy

Místo vydání

Cambridge

Místo konání akce

Praha

Datum konání akce

6. 7. 2015

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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