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

• Published in: Advanced Photonics Nexus 2024-03, Vol.3 (2), p.026009-026009
• Main Authors: Li, Si-Ao, Liu, Yuanpeng, Zhang, Yiwen, Zhao, Wenqian, Shi, Tongying, Han, Xiao, Djordjevic, Ivan B., Bao, Changjing, Pan, Zhongqi, Yue, Yang
• Format: Article
• Language: English
• ISSN: 2791-1519; 2791-1519
• DOI: 10.1117/1.APN.3.2.026009

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.