An Optical Performance Monitoring Model Based on RBFANN Trained with Eye-Diagram

An optical performance monitoring model based on radial basis functions artificial neural network was proposed in this paper. This proposed model can simultaneously identify three kinds of impairments, namely optical signal-tonoise ratio, chromatic dispersion, and polarization-mode dispersion. These impairments were the main cause for optical channels quality deterioration in high bit-rate and transparent optical communication systems. Firstly, the structure of the network was optimized by appliance of Gram-Schmidt rule. Optimization of the network's parameters was realized based on particle swarm optimization method. Then this optimized network was trained and validated with two different data sets derived from eye-diagrams under different levels of aforementioned impairments and bit rates, respective. Finally, the effectiveness of the model was verified by two different optical signals, namely 10 Gb/s non-return-to-zero on-off keying and 40Gb/s return-to-zero differential phase shift keying. The simulation results show that this model had improved performance compared with OPM based on BP-ANN and be transparent for modulation schemes.