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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Numerical Analysis of Artificial Neural Network and Volterra-based Nonlinear Equalizers for Coherent Optical OFDM
Original language description
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
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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