Multiparameter performance monitoring of pulse amplitude modulation channels using convolutional neural networks  

作　　者：Si-Ao Li Yuanpeng Liu Yiwen Zhang Wenqian Zhao Tongying Shi Xiao Han Ivan B.Djordjevic Changjing Bao Zhongqi Pan Yang Yue 

机构地区：[1]Nankai University,Institute of Modern Optics,Tianjin,China [2]University of Arizona,Department of Electrical and Computer Engineering,Tucson,Arizona,United States [3]University of Southern California,Department of Electrical Engineering,Los Angeles,California,United States [4]University of Louisiana at Lafayette,Department of Electrical and Computer Engineering,Lafayette,Louisiana,United States [5]Xi’an Jiaotong University,School of Information and Communications Engineering,Xi’an,China

出　　处：《Advanced Photonics Nexus》2024年第2期75-89,共15页先进光子学通讯（英文）

基　　金：supported by the National Key Research and Development Program of China (Grant No.2019YFB1803700);the Key Technologies Research and Development Program of Tianjin (Grant No.20YFZCGX00440).

摘　　要：A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.

关 键 词：pulse amplitude modulation optical performance monitoring intensity modulation optical fiber communication neural network applications 

分 类 号：TN9[电子电信—信息与通信工程]