Intelligent optical performance monitor using multi-task learning based artificial neural network
Abstract
An intelligent optical performance monitor using multi-task learning based artificial neural network (MTL-ANN) is designed for simultaneous OSNR monitoring and modulation format identification (MFI). Signals' amplitude histograms (AHs) after constant module algorithm are selected as the input features for MTL-ANN. The experimental results of 20-Gbaud NRZ-OOK, PAM4 and PAM8 signals demonstrate that MTL-ANN could achieve OSNR monitoring and MFI simultaneously with higher accuracy and stability compared with single-task learning based ANNs (STL-ANNs). The results show an MFI accuracy of 100% and OSNR monitoring root-mean-square error of 0.63 dB for the three modulation formats under consideration. Furthermore, the number of neuron needed for the single MTL-ANN is almost the half of STL-ANN, which enables reduced-complexity optical performance monitoring devices for real-time performance monitoring.
Keywords
Cite
@article{arxiv.1812.03792,
title = {Intelligent optical performance monitor using multi-task learning based artificial neural network},
author = {Zhiquan Wan and Zhenming Yu and Liang Shu and Yilun Zhao and Haojie Zhang and Kun Xu},
journal= {arXiv preprint arXiv:1812.03792},
year = {2023}
}