Separability of Histogram Based Features for Optical Performance Monitoring: An Investigation Using t-SNE Technique

In this paper, we study the separability of the commonly used features to monitor the performance of optical signal in coherent optical systems. Specifically, our study focuses on the histogram-based features; the asynchronous amplitude histogram (AAH) and the two-dimensional extension of AAH, which we call IQ histogram (IQH). We investigate the conditions under which the optical channel impairments can be monitored. This study utilizes a dimensionality reduction technique, known as the t-distribution stochastic neighbor embedding (t-SNE). Using t-SNE, we show that under certain conditions the histogram-based features cannot be used to distinguish between the high and low impairment effects, rendering these features useless for certain cases, regardless of the type or complexity of the implemented impairments’ estimator/classifier. Extensive simulations results have been conducted to investigate single and multiple impairments conditions and the performance of histogram-based features in each case. The results show that both AAH and IQH can be used for monitoring all types of single Impairment except phase noise in case of AAH. Moreover, under multiple impairments conditions, polarization mode dispersion values can be monitored to some extent, while it is difficult to monitor optical signal-to-noise ratio and chromatic dispersion values especially in the case of concurrent presence of more than two impairments. The results of these investigations are validated by providing a quantitative measure that ties their usefulness to the actual monitoring performance through the estimation of channel impairments under different conditions using linear support vector machine (SVM) regression. It has been found that there is a match to a great extent between the separability investigation using t-SNE and estimation results obtained using SVM.