# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 2 AI-Based QoT and Impairment Modeling
### AI-Based QoT Modeling  
For the QoT modeling, there are many types of metrics, such as BER, Q-factor, SNR, OSNR and margin. The aim of the QoT modeling is to precisely estimate the link performance and then build low margin networks. The requirement of the QoT estimations differs in different scenarios. Some need to judge whether one light path can be established or not [4; 21; 38], and some need the specific value of the QoT metrics. For the former, ML classification methods [43] can be used such as K-nearest neighbors (KNN), random forests (RF), support vector machine (SVM), logistic regression (LR), ANN and so forth. For the latter, ML regression methods [43; 44] can be employed such as network Kriging (NK), Gaussian process (GP), CNN [45], ANN and so forth. We provided a review of some recent ML-based QoT modeling techniques in the literature for different metrics in this section. They are listed in Table 1 and elaborated as follows. For the BER estimation, in [4], an ML-based classifier is used to decide whether the BER of an unestablished lightpath can achieve the network requirement. Features of the model are the traffic volume, modulation format, lightpath total length, length of the longest link and number of lightpath links. The training dataset is obtained from the deployed lightpaths. The employed ML classifier algorithms are KNN and RF with various kernel settings. Moreover, this work comprehensively \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Modeling** & **Algorithms** & **Input Features** \\ \hline BER & K-Nearest Neighbors, Random Forest [4] & Traffic volume, modulation format, total length of links, length of the longest link, number of lightpath links \\ \cline{2-3}  & Stochastic Gradient Descent Polynomial Regression [21] & Generalized OSNR, baud rate, modulation format, FEC, slot-size \\ \cline{2-3}  & Deep Graph Convolutional Neural Networks [46] & Total length of the path, span length, central frequency, number of slots in each path, modulation format, number of Erbium-doped fiber amplifier (EDFA), number of links, BER \\ \hline Q-factor & Case-based Reasoning [47; 48] & Route, selected wavelength, total length of the path, sum of the co-propagating lightpaths per link, standard deviation of the number of total co-propagating lightpaths \\ \cline{2-3}  & Transfer Learning [33] & Channel loading, per-channel output power \\ \hline OSNR & Network Kriging, \(l_{2}\)-norm Regularization [49] & Average PMD of each link, accumulation value of CD, the self-phase modulation (SPM) quantified through the nonlinear phase of the signal \\ \cline{2-3}  & Gaussian Process Regression [50] & Wavelength, OSNR of the established wavelength \\ \hline SNR & Combination of Machine Learning and Physical Layer Model [10] & Lightpath length, link load, number of crossed EDFAs \\ \cline{2-3}  & Gradient Decent [51] & Power, noise figure \\ \hline Margin & K-Nearest Neighbors, Linear Regression, Support Vector Machine, Artificial Neural Networks [38] & Number of hops, number of spans, total link length, average link length, maximum link length, average span attenuation, average CD \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the machine learning (ML)-based quality of transmission (QoT) modeling techniques discussed in Section 2.2.  
compare the performance of different ML algorithms. The influences of different combinations of input features and different sizes of dataset are also analyzed. The result shows that RF outperformes KNN in accuracy and efficiency in most cases. The result also shows that a bigger dataset can help to reach a higher accuracy. In [21], the generalized optical signal-to-noise ratio (gOSNR), baud rate, modulation format, FEC, slot-size and so on are used to estimate the BER and the training data is obtained from a practical system. Therefore, this model can enable controllers to find the optimum configuration of a light path for each specific network.