Comparison of Algorithms Used for Training of Artificial Neural Network Based Nonlinear Equalizer in a Coherent Optical Orthogonal Frequency Division Multiplexing System

9 Pages Posted: 8 Apr 2020

See all articles by Gurpreet Kaur

Gurpreet Kaur

Indian Institute of Information Technology, Una

Gurmeet Kaur

Punjabi University

Date Written: April 7, 2020

Abstract

Artificial neural network based nonlinear equalizers (ANN-NLEs) are one of the efficient techniques of nonlinearities mitigation in coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. Until now, many techniques have been used for the performance enhancement of a CO-OFDM system. Despite this, no one has studied different training algo-rithms of ANN in ANN-NLE. This paper compares training algorithms under five categories i.e., Gradient Descent, Resilient Backpropagation, Conjugate Gradient, Quasi-Newton and Levenberg Marquardt to facilitate the choice of the optimum training algorithm for nonlinearities mitigation. The comparison is shown on the basis of different parameters i.e., Q-Factor, OSNR, and BER.

Keywords: Artificial Neural Network- Nonlinear equalizer (ANN-NLE); Co-herent Optical Orthogonal Frequency Division Multiplexing (CO-OFDM)

Suggested Citation

Kaur, Gurpreet and Kaur, Gurmeet, Comparison of Algorithms Used for Training of Artificial Neural Network Based Nonlinear Equalizer in a Coherent Optical Orthogonal Frequency Division Multiplexing System (April 7, 2020). Proceedings of the International Conference on Advances in Electronics, Electrical & Computational Intelligence (ICAEEC) 2019, Available at SSRN: https://ssrn.com/abstract=3570545 or http://dx.doi.org/10.2139/ssrn.3570545

Gurpreet Kaur (Contact Author)

Indian Institute of Information Technology, Una ( email )

Una, HP
India

Gurmeet Kaur

Punjabi University ( email )

Patiala
India
Patiala, IN Punjab 147002
India

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