基于卷积神经网络的光纤通信非线性失真补偿  

Nonlinear distortion compensation of fiber communication based on convolutional neural networks

作　　者：邱春红[1] QIU Chunhong(School of E-commerce and Logistics,Jiangsu Vocational College of Business Publications Department,Nantong 226000,China)

机构地区：[1]江苏商贸职业学院电子商务与物流学院,江苏南通226000

出　　处：《光学技术》2021年第6期722-727,共6页Optical Technique

基　　金：江苏高校哲学社会科学研究项目(2019SJA1544)。

摘　　要：针对光纤通信高速传输过程中的非线性效应问题,基于卷积神经网络提出一种光纤通信非线性失真补偿方法。利用卷积神经网络捕捉光纤传输信号的非线性变化特征,在网络最后一层通过回归层对光信号进行非线性拟合;通过量子粒子群优化算法搜索深度卷积神经网络的超参数集,降低卷积神经网络的训练难度。数值仿真实验结果表明,量子粒子群优化算法能够有效地优化卷积神经网络的超参数,并且所训练的卷积神经网络能够改善光纤传输的通信质量。In view of the nonlinear effects of high-speed fiber communication transmission,a nonlinear distortion compensation method for fiber communication based on convolutional neural networks is proposed.The proposed method takes advantage of classical convolutional neural networks to capture the nonlinear features of signals transmission through fiber,and utilizes a regression layer as the final layer to realize nonlinear fitting for optical signals.Besides,quantum particle swarm optimization algorithm is adopted to search the super parameters of deep convolutional neural networks,in order to reduce the difficulty of convolutional neural networks training.Numerical simulation experimental results show that the quantum particle swarm optimization can optimize the super parameters of convolutional neural networks,at the same time,the trained convolutional neural networks can improve the communication quality of fiber transmission.

关 键 词：光纤通信 卷积神经网络 量子粒子群优化 信道扰动 光纤色散 

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]