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

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.