Abstract

It is necessary to guarantee the operational range of machine learning (ML)-based optical physical-layer monitors (OPMs). To declare high-level monitoring objectives and obtain their values from OPMs, finding a methodology to accurately estimate the value of a target quantity and ensure their operational range is necessary. We introduce a deep neural network (DNN) with a digital coherent receiver to ML-based OPMs to deal with the abundance of training data needed for convergence and the pre-processing of input data by human engineers needed for feature (representation) extraction. However, guaranteeing the operational range of trained models on DNN-based OPMs was left for another investigation. To address this issue with DNN-based OPMs, we propose an “operational range expander,” a simple treatment of the link between pre-processing training datasets and their specified operational range. We assess the operational range expander by performing simulation and experimentation using a DNN-based optical signal-to-noise ratio (OSNR) estimator. We select a laser frequency offset between a signal and a local oscillator in digital coherent receivers as an example quantity for a practical operational range expander in this study. This is because the OPMs need to work before digitally compensating frequency offset despite the difficulty in fully controlling frequency offset in practical situations. We evaluate bias errors and standard deviations of OSNR estimation from different frequency offsets ranging from 3.5 to +3.5  GHz and confirm that the provided operational range expander specified the operational range of DNN-based OSNR estimators through their training phase.

© 2018 Optical Society of America

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