Soft failure detection and identification in optical networks using cascaded deep learning model

Abstract

Due to malfunction of network devices and surge in physical layer impairments, the quality of transmission (QoT) in backbone optical networks may degrade. If the cause of the degradation is not timely diagnosed and addressed adequately, it may deteriorate into a hard failure. In this study, we consider the external cavity laser (ECL) malfunction-, erbium-doped fiber amplifier (EDFA) malfunction-, and nonlinear interference-related soft failures. We propose a software-defined optical network (SDON)-based soft failure detection and identification strategy using a cascaded deep learning model. Time-series QoT data of normal and degraded lightpaths obtained through the optical performance monitoring equipment is used to train the proposed cascaded deep learning model. In the first stage, a long short-term memory-based autoencoder (LSTM-AE) model is used as a binary classifier to identify the anomalous time-series sequences. Subsequently, an LSTM-based multiclass classifier is used to identify the type of soft failure. Our proposed approach shows an accuracy of 99.70%.