Optical performance monitoring based on reconstructed eye diagrams and artificial neural networks

A novel optical performance monitoring (OPM) method based on asynchronous optical-sampling, eye diagram reconstruction and artificial neural network (ANN) is presented. Firstly, the monitored optical signal is optically sampled in asynchronous way, and the eye diagrams are reconstructed by software-synchronized algorithm. Secondly, the features of reconstructed eye diagrams are extracted to train the artificial neural network. Finally, the outputs of the trained neural network are used to monitor optical signal impairments. Simulations of optical signal noise ratio (OSNR) and chromatic dispersion (CD) monitored in 10 NRZ-OOK, 40 Gbit/s RZ-OOK and 40 Gbit/s RZ-DPSK systems are presented. The monitoring results show that the accuracy of this proposed OPM method is higher, the correlation coefficient between neural network output and test data is greater than 0.98, and the impairment monitoring average error is less than 5%.