Reliable Optical Performance Monitor: The Combination of Parallel Framework and Skip Connected Generative Adversarial Network

Abstract

Future optical network is developing towards highly heterogeneity and flexibility, which means that the various signals will be transmitted in the network and the optical performance monitor is more likely to encounter the signal beyond its monitoring range. When the signal beyond monitoring range (abnormal data) is input, the conventional optical performance monitoring (OPM) framework without the ability of data filtering will produce completely wrong results. Although the serial OPM framework has the ability of data filtering, it increases the processing time cost. We propose a novel parallel OPM framework, in which the judgement and analysis modules process the input data simultaneously to reduce the time cost. Moreover, the light-weight and high-performance skip connected generative adversarial network (GAN) trained only on the normal data (within the monitoring range) is proposed in the judgement module to filter the abnormal data in a fast-speed way (~9 ms). In the simulation, eight common signals are used to test the performance of the skip connected GAN in the judgement module. The optimal area under the curve (AUC) value of 0.952 is obtained when the abnormal data is defined as 60 Gbps 64QAM signal. Besides, the impact of the latent vector length, the task weights, the weight of abnormal score, shifted K values and training data size on the model performance are studied.

Keywords

• Generative adversarial network (GAN)
• optical performance monitoring (OPM)