# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 4 Experimental Results and Discussion
### Model Evaluation
#### 4.3.2 Evaluation Results at 26 GHz Shifts  
1. Results with TRDS data only  
Figure 18 shows the evaluation results at the 26 GHz shift without augmentation. The number of training data points used was 13,000 for each label. The number was almost the same as the one used for detection at the 51 GHz shift. As shown in Figure 18, the detection accuracy was severely degraded in comparison with the results at 51 GHz shifts. The degradation in detection accuracy at 26 GHz shifts may be a result of some characteristic features that correspond to optical filter shifts becoming fainter than in the case of 51 GHz shifts. To improve the detection accuracy, we challenged to augment the constellation data by adopting the above-mentioned novel DC-GAN-based data reproduction scheme. Figure 17: Evaluation result at 51 GHz without augmentation (DS: 12,450). Figure 18: Evaluation result at 26 GHz without augmentation (DS: 13,000). Results with augmented data only  
Next, an evaluation experiment was conducted using only the constellation data generated by the DC-GAN, as shown Figure 19. The evaluation experiments used two dataset sizes: 13,000, which matches the number used in the 26 GHz shifting experiment without augmentation, and an increased count of 28,000 generated by the DC-GAN. As evident from the evaluation results, when the evaluation experiment was conducted using the same dataset size of 13,000 as in the first experiment, the overall performance was not satisfactory, as shown in Figure 19a, despite some bias being observed among the three labels. However, when the dataset size was increased to 28,000, the overall score showed improvement, as one can see in Figure 19b, although the results remained suboptimal as an evaluation outcome. However, these findings confirm that the constellation data generated by the DC-GAN are treated equivalently to the data obtained directedly from the receiver LSI, thereby demonstrating their independence. Additionally, it was observed that the evaluation results showed proportional improvement with an increase in the size of the datasets. This evaluation is serves to clarify whether the GAN-generated data could be used for machine learning of an optical filter shift. We successfully confirmed its validity through this evaluation experiment. 3. Results with TRDS data and GAN-augmented data  
Finally, an evaluation experiment was conducted by combining the constellation data obtained through the conventional method with the data generated by the DC-GAN, as shown Figure 20. The dataset consisted of 13,000 TRDS data points and 28,000 DCGAN-generated data points for each of the three labels, resulting in a total of 41,000 data for evaluations. The amount of optical filter shift is 26 GHz. Figure 19: Evaluation result at 26 GHz; only augmented data; (a) DS: 13,000; (b) DS: 28,000. As shown above, by incorporating the constellation data generated by the DC-GAN and increasing the number of datasets used in the evaluation experiment, we achieved around 90 to 100% of precision, recall and F1-score, even with a small optical filter shift. Moreover, 68% of the total training data used in this evaluation experiment consisted of constellation data generated by the DC-GAN. As a result, we successfully augmented constellation data from the original data obtained by TRDS. This was our goal in this paper. We achieved sufficiently high accuracy in this specific use case; so, we do not need further optimization of GAN models. This demonstrates the effectiveness of our approach in reducing the amount of actual training data volume required for optical impairment sensing while simultaneously enhancing the corresponding sensitivity. Further optimization of our data augmentation technique will be expected to yield a larger reduction in the data volume required for machine learning and will be able to minimize the disruption of actual production network for data acquisition.