基于深度神经网络的光纤通信高阶色散预测  

High-order dispersion prediction of fiber communication based on deep neural networks

作　　者：杨恺[1] YANG Kai(School of Electronic and Electrical Engineering,Dongguan Polytechnic,Dongguan 523808,China)

机构地区：[1]东莞职业技术学院电子与电气工程学院,广东东莞523808

出　　处：《光学技术》2021年第6期716-721,共6页Optical Technique

基　　金：东莞市社会发展科技面上项目(20211800900602);广东省普通高校重点领域专项(2020ZDZX3091);东莞职业技术学院科研基金项目(2020a20)。

摘　　要：高阶色散导致光信号在光纤的传播过程中常常会发生非线性失真的现象,并导致接收信号的误码率升高。为了解决该问题,提出了基于深度神经网络的光信号预测技术。该技术首先总结了光纤通信系统的信号传播模型,然后确定了深度神经网络输入层与输出层的参数,最后在训练阶段通过海洋捕食者算法搜索神经网络隐藏层的参数。实验结果表明,深度神经网络模型能够准确地预测接收光信号的正确波形,为光纤的色散补偿提供基础。Because of the existence of high order dispersion,non-linear distortion of optical signals usually appears during the fiber propagation process,it results in the increase of bit error rate of received signals.In view of this,a prediction algorithm of optical signals based on deep neural networks is proposed.This algorithm summarizes the signal propagation model of fiber communication system first,then decides the parameters of input layer and output layer for the deep neural networks.Finally,based on Marine Predators Algorithm the algorithm searches the parameters of hidden layers of neural networks during the training phase.Experimental results show that the deep neural networks can predict the correct wave of received optical signals,and it can provide foundation for dispersion compensation of fibers.

关 键 词：光纤通信 色散补偿 非线性传播 深度神经网络 海洋捕食者算法 波形失真 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术] TH691.9[自动化与计算机技术—计算机科学与技术]