Multi-parameter optical performance monitoring based on single-channel convolutional neural network

For the first time, a combination of grayscale maps and single-channel convolutional neural networks (CNN) is used in the direct detection optical performance monitoring (OPM) technique. Due to the faster monitoring speed of the direct detection OPM technique, it is very suitable for intermediate nodes of optical networks. The OPM technique proposed in this paper is an improvement based on the previous asynchronous delay-tap plot (ADTP) techniques. By reducing the redundant color channel information, the training process of the ADTP feature-based monitoring model is improved. In the simulation stage, three high-order modulated signals, 16quadrature amplitude modulation (QAM), 32QAM and 64QAM, are used as sample signals. The ADTP color maps, ADTP pseudo-color maps and ADTP grayscale maps generated by these three modulated signals with different signal-tonoise ratios and cumulative dispersion (CD) are collected. The accuracy of the monitoring model based on these three feature maps is close to 100% for both cumulative dispersion identification and signal modulation formats identification (MFI). However, the monitoring models based on grayscale maps and pseudo-color maps perform slightly better in estimating optical signal-to-noise ratio (OSNR) compared to the monitoring models based on color maps. And the grayscale map-based model slightly outperforms the other two models in terms of training complexity and monitoring speed. The superiority of grayscale maps and single-channel convolutional neural networks compared to previous ADTP-based OPM techniques is demonstrated.