# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 3 AI-Based Optical Performance Monitoring
### AI-Based QoT and Impairment Monitoring  
For the QoT monitoring, the evaluation of BER, SNR, Q-factor, QSNR and so forth can enable controllers to assess the transmission performance of each established light path and provide a quantitative measure to check whether the designed QoT can be ensured. At the same time, impairment monitoring is also needed to provide an insight into each specific effect in the physical layer. In this section, various applications of ML for monitoring QoT and impairment are discussed. A brief summary of methods discussed in this section is shown in Table 3 and the details are elaborated as follows. In [14], an ANN is used to monitor the OSNR, CD and PMD simultaneously with empirical asynchronously sampled signal amplitudes. In [57], to make an easier monitoring procedure without labor-intensive feature engineering, deep neural networks (DNN) are used to monitor the OSNR with asynchronously sampled raw data. For this work, neural networks with an advanced structure perform the feature extraction and monitoring calculation at the same time. Moreover, the results show that a larger training dataset and a deeper neural network can help to increase the estimation performance. As more advanced neural network structures emerge, CNN is also introduced to monitor the OSNR and modulation format simultaneously [13; 58; 59]. In [37], ANN is adopted to monitor the OSNR based on the historical data collected from real systems. In [60], principle component analysis (PCA) and ANN are used to monitor the OSNR, bit rate, modulation format, CD and DGD by asynchronous delay-tap plots. In this case, PCA can reduce the number of input parameters, thus reducing the complexity of the ANN. A similar approach is investigated in [61] to monitor the OSNR and identify the modulation format by asynchronous single channel sampling, which makes the algorithms simple and low-cost. In some other situations, ML-methods are also employed to monitor specific impairments. In [62], DNN is proposed to monitor the OSNR and modulation format with signals' amplitude histograms. This method only requires few DSP blocks, which makes it cost-effective for deployment. In [63], kernel-based ridge regression is used to monitor the CD and differential group delay (DGD) simultaneously. This method is validated by simulations and experiments. In [64], the long short-term memory (LSTM) neural network is applied to monitor the OSNR with the four-ributary digital outputs. The mean absolute error can be significantly reduced from 0.4 to 0.04 dB compared with other ML algorithms. In [65], OSNR and nonlinear noise power are monitored simultaneously based on frequency domain signals. In [66], to identify the impairment causing the transmission degradation, SVM can accurately make classifications between CD, PMD and noncoherent crosstalk. In many scenes, obtaining specific features strongly related to an impairment can improve monitoring accuracy. In [68], the amplitude noise correlation (ANC) and phase noise correlation (PNC) are proved to be related to nonlinear impairments and an ANN is applied to monitor the nonlinear SNR based on them. In [69], multiple logarithmic ANCs are directly input to an estimator using support vector regression for monitoring the nonlinear SNR, which can estimate nonlinear noise without features like the number of WDM channels. Moreover, in [5], the ANC and PNC are combined with an analytical model such as the GN model to estimate nonlinear noise. Simulation results in [5] prove that this combination can improve the monitoring accuracy.