Optical performance monitoring in 40-Gbps optical duobinary system using artificial neural networks trained with reconstructed eye diagram parameters

A technique using artificial neural networks trained with parameters derived from reconstructed eye diagrams for optical performance monitoring in 40-Gbps optical duobinary (ODB) system is demonstrated. Firstly*the optical signal is asynchronously sampled by short pulse in the nonlinear medium such as semiconductor optical amplifier and highly nonlinear fiber*the sampled and collected data is then processed by improved software synchronization algorithm to obtain reconstructed eye diagrams without data clock recovery. Secondly*the features of the reconstructed eye diagrams are extracted to train the three-layer preceptor artificial neural network. Finally*the outputs of trained neural network are used to monitor multiple optical signal impairments. Simulation experiments of optical signal noise ratio (OSNR)*chromatic dispersion (CD) and polarization mode dispersion (PMD) monitoring in 40-Gbps ODB system is presented. The proposed monitoring scheme can accurately identify simultaneous impairment with the root-mean-square (RMS) monitoring error less than 3%.