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

The recent study on artificial neural network signal equalization authored by Ahmed Galib Reza (KAIST EE) and June-Koo Kevin Rhee has been published in IEEE Photonics Technology Letters.  ( https://ieeexplore.ieee.org/document/8401897 )

Article Content:

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

Authors: Ahmed Galib Reza, and June-Koo Kevin Rhee

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.

 

 

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