# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 1 Introduction
For example, the filtering effect brought by reconfigurable optical add-drop multiplexers (ROADM) can be modeled with an ANN [9]. Finally, many data-driven methods with ML can be adopted to adjust analytical models to be scalable for more scenarios where they show large deviations. For instance, in [10], ML algorithms are used to improve the performance of the analytical model with data collected from an established lightpath. Figure 1: Applications of modeling and monitoring techniques in optical networks. The transmission performance of an established light path is not always reliable due to the various changes of link conditions. Therefore, optical performance monitoring (OPM) is a key building block, which enables network controllers to adjust link configurations according to the real-time status of a system. Moreover, monitoring results can be used to detect, identify and localize failures in EON's. However, the heterogeneity of EON's has also raised many new challenging requirements for the monitoring techniques, and ML shows a potential in building more intelligent and efficient monitoring schemes. Firstly, faster response time is desired for monitoring [2]. Since a monitoring agent should provide information for optimizing lightpath configurations and diagnosing the anomaly, the monitoring scheme needs to be capable of tracking the change of the network performance. According to [2], the monitoring time of some network applications is required to be at the order of milliseconds. Therefore, some traditional methods with complex data processing and a long-time window may not be compatible with dynamic real-time applications. To solve this problem, advanced ML methods with forward propagation mechanisms [11], such as ANN, convolutional neural networks (CNN) and so on, can be employed to accomplish the feature extraction and estimate real-time status in a short time period [5; 12; 13]. These monitoring tools can be trained offline before deployment. When estimating the signal performance, the pre-trained monitors can respond in a very short time. Secondly, monitoring techniques should be cost-effective [2]. In particular, they should not necessitate expensive external devices, and one OPM block is preferable to monitor multiple impairments. It may be difficult for analytical models to achieve these two goals simultaneously but ML-aided methods can help to fulfill these requirements. For instance, samples of received signals can be input to ML algorithms for monitoring the chromatic dispersion (CD), polarization-mode dispersion (PMD) and optical signal-to-noise ratio (OSNR) at the same time [14]. Moreover, when obtaining information from the receiver digital signal processing (DSP) modules, ML methods may be able to monitor the QoT or impairments without any external devices such as the optical spectrum analyzer (OSA) [15]. Therefore, for the next generation EON, applications of ML techniques for modeling and monitoring can provide strong support to build a reliable and intelligent optical network with lower design margins. This paper is intended to review recent progress in AI-enabled modeling and monitoring techniques for EON. Since optical networks are full of data with heterogeneous sources and various characteristics, it is possible to improve the accuracy and/or sensitivity of optical performance estimation functionalities with these data. However, the large number of data also makes it more challenging to discover useful information from them. In this case, data-driven ML methods are essential tools for network planning and management, but these methods should be improved to be cost-effective and reliable for deployment. Several previous review works have provided comprehensive summaries of the applications of ML techniques in optical networks [2; 16; 17; 18; 19]. They discuss the ML-based techniques adopted in various domains and point out many possible directions for the future deployment strategies.