Low Complexity Convolutional Neural Networks for Equalization in Optical Fiber Transmission

TL;DR

This paper introduces a low complexity convolutional neural network designed for optical fiber transmission equalization, significantly reducing parameters and improving mean squared error over traditional methods.

Contribution

The paper presents a novel CNN architecture that reduces complexity while enhancing equalization performance in optical fiber systems.

Findings

Five-fold reduction in trainable parameters.

3.5 dB improvement in MSE over digital backpropagation.

Comparable complexity to existing equalizers.

Abstract

A convolutional neural network is proposed to mitigate fiber transmission effects, achieving a five-fold reduction in trainable parameters compared to alternative equalizers, and 3.5 dB improvement in MSE compared to DBP with comparable complexity.