摘要
A technique using artificial neural networks trained with parameters derived from delay tap plots for optical performance monitoring in 40 Gbit/s duobinary system is demonstrated. Firstly, the optical signal is delay tap sampled to obtain two-dimensional histogram, known as delay tap plots. Secondly, the features of delay tap plots are extracted to train the feed forward, three-layer preceptor structure artificial neural networks. Finally, the outputs of trained neural network are used to monitor optical duobinary signal impairments. Simulation of optical signal noise ratio ( OSNR), chromatic dispersion (CD), and differential group delay (DGD) monitoring in 40 Gbit/s optical duo- binary system is presented. The proposed monitoring scheme can accurately identify simultaneous impairments without requiring synchronous sampling or data clock recovery. The proposed technique is simple, cost-effective and suitable for in-service distributed OPM.
A technique using artificial neural networks trained with parameters derived from delay tap plots for optical performance monitoring in 40 Gbit/s duobinary system is demonstrated. Firstly, the optical signal is delay tap sampled to obtain two-dimensional histogram, known as delay tap plots. Secondly, the features of delay tap plots are extracted to train the feed forward, three-layer preceptor structure artificial neural networks. Finally, the outputs of trained neural network are used to monitor optical duobinary signal impairments. Simulation of optical signal noise ratio ( OSNR), chromatic dispersion (CD), and differential group delay (DGD) monitoring in 40 Gbit/s optical duo- binary system is presented. The proposed monitoring scheme can accurately identify simultaneous impairments without requiring synchronous sampling or data clock recovery. The proposed technique is simple, cost-effective and suitable for in-service distributed OPM.
基金
Supported by the National Natural Science Foundation of China (60978007
61027007
61177067)