Simultaneous monitoring of the values of CD, Crosstalk and OSNR phenomena in the physical layer of the optical network using CNN

Abstract

The aim of the research was to explore the possibilities of using the Asynchronous Delay Tap Sampling (ADTS) and Convolutional Neural Network (CNN) methods to monitor the simultaneously occurring phenomena in the physical layer of the optical network. The ADTS method was used to create a data sets showing the combination of Chromatic Dispersion (CD), Crosstalk and Optical to Signal Noise Ratio (OSNR) as optical disturbances in graphic form. Data were generated for 10 GB/s, Non-return-to-zero On–off keying (NRZ-OOK) and Differential Phase Shift Keying (DPSK) modulation and bit delays: 1 bit, 0.5 bit and 0.25 bit. A total of 6 data sets of 62,000 images each were obtained. The learning process was carried out for the number of epochs 50 and 1000. From the obtained learning results of the network, models with the best R^{2} matching factor were selected. The learned models were further used to study the recognition of three phenomena simultaneously. The tests were carried out on sets of 2500 images in a combination of interference in the following ranges: 400–1600 ps/nm for CD and 10–30 dB for Crosstalk and OSNR. Very good results were obtained for recognizing simultaneously occurring phenomena using models learned up to 1000 epoch. Accuracy of over 99% was obtained for CD and Crosstalk for both modulations. In the case of the OSNR phenomenon, slightly weaker results were obtained above 96% in most cases. For models taught up to 50 epoch, very good results were obtained for the CD phenomenon (over 99%). For Crosstalk weaker results for OOK modulation were obtained. Poor results were obtained for the OSNR phenomenon, where recognition accuracy ranged from 50 to 80%, depending on the type of modulation and bit delay. Based on the conducted research, it was established that the use of ADTS and CNN methods enables monitoring of simultaneously occurring CD, Crosstalk and OSNR interference in the physical layer of the optical network, while maintaining the requirements for Optical Performance Monitoring systems. These requirements are met for network models learned up to 1000 epoch.