  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.
