Intelligent optical performance monitor using multi-task learning based artificial neural network

TL;DR

This paper presents a multi-task learning neural network that simultaneously monitors OSNR and identifies modulation formats in optical signals, achieving high accuracy and reduced complexity for real-time optical performance monitoring.

Contribution

The study introduces a multi-task learning neural network that outperforms single-task models in accuracy and efficiency for optical performance monitoring tasks.

Findings

MFI accuracy of 100% for three modulation formats

OSNR monitoring RMSE of 0.63 dB

Reduced neuron count by nearly half compared to single-task models

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.