# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 2 AI-Based QoT and Impairment Modeling
### AI-Based QoT Modeling
In [46], a deep graph convolutional neural network (DGCNN) is applied to estimate the feasibility of the network state. This work considers the crosstalk between unestablished and established light paths according to historical data. For the Q-factor estimation, in [47; 48], a cognitive QoT estimator classifies lightpaths to high- or low-quality categories. The classification method is case-based reasoning (CBR), which is based on the prior experiences or cases to make estimations. Features for this model include the route, selected wavelength, total length of a path, sum of the co-propagating lightpaths per link and standard deviation of the number of total co-propagating lightpaths. To extend a pre-trained model to more scenarios, transfer learning is proposed in [52] to make use of collected data from new scenes for retraining. This method can effectively reduce the training time when configurations of the optical networks change. Methods mentioned above all use historical data from real scenes and they all achieve a good performance for estimating the QoT. Therefore, we can infer that data-driven ML methods can improve the training efficiency and the scalability of models to more systems. For the OSNR estimation, in [49], regression methods like network Kriging (NK) and least-squares minimization with \(l_{2}\)-norm regularization are utilized. The parameters used for estimations are the average PMD of each link, accumulation value of CD, and SPM quantified through the nonlinear phase of the signal. The algorithm is based on established light paths to evaluate an unestablished path for transparent optical networks. This method successfully helps to design a reliable light path efficiently. According to [50], in some practical systems, the noise figure and gain of amplifiers and fiber loss are wavelength-dependent. In this case, the Gaussian process regression (GPR) is used to estimate the OSNR with a confidence output. For the SNR estimation, in [10], the combination of the ML model (ML-M) and physical layer model (PLM) is applied to build a framework called ML-PLM to estimate the QoT performance. This model is based on the data from the existing connections of a network. Features used for estimation are the light path length, link load and number of crossed Erbium-doped fiber amplifiers (EDFAs). The simulation shows that, this method can reduce the influence of the uncertainty of parameters such as the fiber attenuation, dispersion, nonlinear coefficients or amplifier noise. Moreover, the more light paths the model can get from the network topology, the higher accuracy the model can achieve. In this way, ML-PLM can reach better performances and makes the model suitable for a dynamic network. In [51], gradient decent is used to correct the deviations of the input parameters for the QoT estimators. This method takes advantage of back-propagation algorithms embedded in many neural networks, which successfully reduces the uncertainty of models. For the margin estimation, in [38], ML models such as KNN, LR, SVM and ANN are proposed to judge whether the residual margin is positive. The input features are the number of hops, number of spans, total link length, average link length, maximum link length, average span attenuation and average dispersion. To build a better classifier, those models for classification are investigated with different kinds of kernels. Then, to obtain the specific value of the residual margin an ANN is employed. In [38], the performances of the adopted ML algorithms are compared with each other and they all reach a decent performance.