Enhancing the Credibility of the Optical Performance Monitor With Adversarial Training

Abstract

The existing optical performance monitoring (OPM) scheme based on deep neural network has no selection capability of the input data. They always accept and process all, which may result in serious monitoring errors and reduce the credibility of the monitoring system. Because the transmitted data in the future heterogeneous fiber-optic networks are diverse, and it's likely to exceed the scope of the monitoring system. We propose an unsupervised generative adversarial network (GAN) as the judgement module in the new OPM framework to select the legal data within the scope of the monitoring system. The generator consists of encoder-decoder-encoder (EDE) sub-network, jointly learns the image and latent feature distribution of the legal data. And the training data for the network in the new added judgement module is the same as the OPM analyzer network's, therefore, no extra data are collected, which is low-cost. In the simulation, four modulation formats under two bit-rates are taken into account to verify the model performance in the judgement module. When 60 Gbps 64QAM signal is selected as illegal data, the max value of the area under the curve (AUC) is 0.942. The judgement time for single image is about 12 ms. Moreover, the influence of the task weights and the latent feature shape on the judgement performance are investigated. The new added judgement module largely increases the credibility and safety of the existing OPM scheme.

Keywords

• Optical performance monitoring (OPM)
• generative adversarial network (GAN)