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With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

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. If these soft failures are not handled properly and timely, service disruption may occur. Identifying the cause of soft failure is a key step to restore the degraded links. However, it is difficult for traditional methods to accomplish this task. Fortunately, powerful machine learning (ML) algorithms provide a promising path to address this problem. In this article, a novel two-stage soft failure identification scheme based on a convolutional neural network (CNN) and receiver DSP is proposed. The input of the CNN is the power spectrum density (PSD) extracted from a coherent receiver, and the output contains the identified cause of soft failures together with their probabilities. Extensive simulations are performed to validate the proposed method. Four types of soft failure causes are explored including the offset of optical filter's center frequency (FS), the tightening of optical filter's 3-dB bandwidth (FT), SNR degradation due to the increased amplified spontaneous emission (ASE) noise, and the Kerr nonlinear effect. When only one soft failure cause exists, excellent accuracy is achieved. When multiple soft failure causes exist, the probabilities of these causes provided by the CNN are used to gain insight into their influences on the system. Finally, we investigate the interpretation of the CNN and a reasonable interpretation is given and discussed.

We propose a receiver DSP based scheme to localize WSS anomaly in an optical link. Through extensive simulations, we show that the accuracy reaches up to 96.4% with a good generalization performance.

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