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

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.