Nonlinear Equalizer Based on Neural Networks for PAM-4 Signal Transmission Using DML

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Nonlinear distortion from a directly modulated laser (DML) is one of the major limiting factors to enhance the transmission capacity beyond 10 Gb/s for an intensity modulation direct-detection optical access network. In this letter, we propose and demonstrate a low-complexity nonlinear equalizer (NLE) based on a machine-learning algorithm called artificial neural network (ANN). Experimental results for a DML-based 20-Gb/s signal transmission over an 18-km SMF-28e fiber at 1310-nm employing pulse amplitude modulation (PAM)-4 confirm that the proposed ANN-NLE equalizer can increase the channel capacity and significantly reduce the impact of nonlinear penalties.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2018-08
Language
English
Article Type
Article
Citation

IEEE PHOTONICS TECHNOLOGY LETTERS, v.30, no.15, pp.1416 - 1419

ISSN
1041-1135
DOI
10.1109/LPT.2018.2852327
URI
http://hdl.handle.net/10203/244810
Appears in Collection
EE-Journal Papers(저널논문)
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