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 lightweight 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. INDEX TERMS generative adversarial network (GAN), optical performance monitoring (OPM).

Survey on Applications of Machine Learning in Low-Cost Non-Coherent Optical Systems: Potentials, Challenges, and Perspective

“…The ADTP features have been used for multiple impairment monitoring methods such as OSNR, CD, and PMD, as in [37][38][39][40]. In [41][42][43], the authors proposed the asynchronous single channel sampling (ASCS) feature method, which is more cost-effective than ADTP. In this method, the signal s(t) is sampled asynchronously using one tap, after which the samples are shifted by k samples and utilized to build the sample pairs s[i] and s[i + k] phase portrait.…”

“…In this method, the signal s(t) is sampled asynchronously using one tap, after which the samples are shifted by k samples and utilized to build the sample pairs s[i] and s[i + k] phase portrait. The generated phase portraits can be used as input images to ML algorithms, as in [41,43].…”

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

“…In this method, shown in Figure 8, the signal y(t) is sampled asynchronously using one tap, and then the samples are shifted by k samples and the sample pairs y i (t) and y i+k (t) used to construct a phase portrait. This method is less expensive than two-tap sampling (Yu et al, 2014;Fan et al, 2019;Fan et al, 2020).…”

“…The generated phase portraits can be used as images for example in Fan et al (2019) and Fan et al (2020).…”

“…It also makes it difficult to scale the ML algorithm to a different signal type than what it was trained with. To mitigate this, deep learning techniques have been studied where the algorithm can learn its own features from the input data, the commonest way being by supplying it with processed images (Wang et al, 2017;Fan et al, 2019;Fan et al, 2020;Zhang et al, 2020) and the 4tributary output of the coherent receiver. Of course, this comes with more complexity since deep learning algorithms are generally more difficult to train.…”