# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 2 AI-Based QoT and Impairment Modeling  
### Background and Challenges  
QoT modeling for an unestablished lightpath can help planning tools in the control plane to develop proper strategies of routing, wavelength assignment and signal configurations [20; 21; 22; 23; 24; 25]. In EON, during the phase of network planning, the accuracy of QoT and impairment models is influenced by various configurable parameters like modulation format, symbol rate and physical path in optical networks. If these parameters are not accurate, the estimations of QoT may have deviations compared with the real value [5; 26; 27]. In this case, due to the inaccuracy of planning tools, a large design margin [3] is needed and networks are overutilized to avoid network degradation until the EoL. As a result, QoT models with a higher accuracy are desired and impairment models can provide an insight into the contributions of each individual impairment to help QoT estimators reach a better performance. For the QoT modeling, some traditional methods [28] can estimate the performance of an optical link in terms of signal-to-noise ratio (SNR), pre-forward error correction (FEC) bit error rate (BER), OSNR and so forth. For the impairment modeling, traditional methods can estimate some important physical layer effects, such as fiber nonlinearity, optical filtering effect and amplified spontaneous emission (ASE) noise. The requirements for QoT and impairment modeling techniques of the next-generation EON are illustrated as follows. * Self-adaptiveness: Analytical models are essential for estimating the QoT of unestablished lightpaths. However, they may not be scalable for all scenarios since the assumptions for these models may be inappropriate when the configuration of traffic optical paths evolves continuously. For instance, the optical amplifier gain spectrum is wavelength-dependent but some models assume the gain to be identical for all channels. This kind of improper assumption may lead to an inaccurate estimation of the ASE noise. Therefore, network planning tools with self-adaptive QoT and impairment models are highly desired to guarantee a high-quality transmission from the BoL to the EoL. * Efficiency: For many QoT and impairment models, traditional models with high precision may incur burdensome computational requirements. For example, to model the nonlinear impairment, the SSFM [6; 29; 30; 31] can reach a high accuracy if the step size is sufficiently small, which leads to a high complexity. The GN model [7] can provide results in a very short time but the precision is lower than that of the SSFM in most scenarios. Therefore, models that can efficiently make estimations with a high accuracy are desired. * High tolerance to parameter uncertainty: In a practical system, link parameters can be uncertain due to inaccurate measurements and other reasons. If the uncertainty of the model input exists, there might be a significant deviation between the real value and the model estimation [5]. Therefore, models that are less sensitive to parameter uncertainty are also desired. To fulfill these requirements, data-driven ML methods open new opportunities. Firstly, ML methods are mostly data-driven [32], which means they enable the model to learn the characteristics of the dataset, in principle even without any theoretical information [4; 33; 34; 35; 36]. This specific ability of learning adaptively with data allows ML models to be easily extended to any scenarios if the simulation, experiment or field-trial data for this situation can be obtained [13; 23; 37]. Secondly, for most optical networks, the number of tunable parameters for link configurations is limited. Therefore, the number of input parameters for QoT or impairment models are relatively small [5; 33; 38], which enables ML models to reach a good performance with simple structures such as the ANN with a small number of nodes and hidden layers [23]. In this case, these low-complexity ML models can calculate faster compared with some traditional models.