ROADM-Induced Anomaly Localization and Evaluation for Optical Links Based on Receiver DSP and ML

With the advance of elastic optical networks, optical communication systems are becoming more flexible and dynamic. In this scenario, soft failures are more likely to occur due to various link impairments, of which the filter impairment caused by WSS is a major one. If these soft failures are not handled properly and timely, the quality-of-transmission (QoT) will degenerate, even leading to service disruption. During this process, it is important to know the accurate location and the magnitude of soft failure. However, it is difficult for traditional methods to accomplish this target. Fortunately, with the fast progress of the powerful machine learning (ML) algorithms, a new promising way is provided to address this problem. In this article, we propose to use artificial neural network (ANN) and Gaussian process regression (GPR) to localize the soft failure location and estimate anomaly value. The input of the ANN and GPR is extracted from the power spectrum density (PSD) and the equalizer taps, which can be easily obtained from a coherent receiver. We explore two types of soft failure caused by the WSS, including the offset of WSS's center frequency, i.e., frequency shift (FS), and the tightening of WSS's 3-dB bandwidth, i.e., frequency tightening (FT). To validate the proposed method, we perform extensive simulations. The localization accuracy of the ANN can reach 95%, and the mean-absolute-error (MAE) of the GPR can reach 0.1-GHz, demonstrating the effectiveness of the proposed method. Besides, we validate the generalization of the proposed method under different link conditions. Finally, the importance of each input feature is explored, showing the effectiveness of the selected features.