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

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 (NL...

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Bibliographic Details
Published in:IEEE photonics technology letters 2018-08, Vol.30 (15), p.1416-1419
Main Authors: Reza, Ahmed Galib, Rhee, June-Koo Kevin
Format: Article
Language:English
Subjects:
Artificial neural networks
Channel capacity
direct detection
Equalizers
Fiber nonlinear optics
Learning theory
Machine learning
Neural networks
optical access network
Optical fiber networks
Optical fibers
Optical transmitters
Pulse amplitude modulation
Signal transmission
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Summary: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.
ISSN:1041-1135
1941-0174
DOI:10.1109/LPT.2018.2852327