Simultaneous monitoring of the phenomena of CD, Crosstalk and OSNR in the physical layer of the optical network with the use of convolutional neural networks

The article shows the results of the algorithm of Convolutional Neural Networks (CNN) based on data from the Asynchronous Delay Tap Sampling (ADTS) method. The combination of these two techniques made it possible to obtain an expert system to monitor simultaneously impairments (Chromatic Dispersion, Crosstalk and Optical to Signal Noise Ratio) occurring during transmission in the physical layer of the optical network. The ADTS technique allows to present disturbances in the form of images called phase portraits. Using the VPIphotonics program, 62000 images for modulation OOK were generated. All images were disturbed by a different combination of CD, Crosstalk and OSNR phenomena. These combination contain interference in pairs (e. g. 25 ps/nm for CD, 24.5 dB for Crosstalk and 17.5 dB for OSNR) but also individually (e.g. 678.93 ps/nm for CD and 0 for Crosstalk and OSNR). The next stage was the use of the convolutional algorithms of neural network. Research was conducted to teach the network the best possible recognition of simultaneously occurring phenomena. The challenge in the field of monitoring optical networks is monitoring with high accuracy simultaneously occurring phenomena using one method. As a result of the conducted research, high possibilities of simultaneous monitoring of the CD and Crosstalk phenomena for which the R-2 match factor was obtained at the level higher than 0.999 were established. In the case of the OSNR phenomenon, a match factor of over 0.99 was obtained. The conducted research shows large possibilities of using CNN to simultaneously monitor at least three phenomena and encourage to undertake research on the possibilities of simultaneous monitoring of more phenomena with the use of these networks.