# Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition
## 3 AI-Based Modulation Format Recognition
### Results and Discussion
#### 3.2.2 Effect of Different Factors on MFI  
The model's performance under different epochs, transmission distances, and OSNR conditions are studied in this paper. The accuracy of the model under various epochs is shown in Figure 11a. When the number of epochs is too small, the effective features within the dataset are not fully extracted, or the model's parameters are not sufficiently adjusted during the training phase, hence a low accuracy achieved within a short period. The overall trend is an increase in accuracy with the number of epochs, as the model parameters are gradually optimized and more features are extracted, leading to a step-by-step improvement in performance.  
The accuracy reaches 99% at 50 epochs. Further increasing the number of epochs, the performance does not improve further but rather fluctuates around a certain value, due to the model reaching saturation in feature extraction. The MFI accuracy has reached a satisfactory level within the tested range of epochs, proving that the model has strong robustness under the factor of epochs.  
With an increment of 10 km for each step, the transmission distances of the three types of signals vary from 30 km to 180 km. The impact of different transmission distances on the model's MFI is shown in Figure 11b. It can be observed that when the transmission distance is less than or equal to 120 km, the MFI accuracy of the model is unaffected by the distance and remains consistently at 100%. However, at a transmission distance of 130 km, the accuracy begins to decline; the longer the signal transmission distance, the  
Figure 10: MFI confusion matrix for training and testing sets: (**a**) training set output confusion matrix; (**b**) testing set output confusion matrix.  
more severe the signal degradation, the higher BER, and the more pronounced the eye diagram distortion. The damage to the signal due to transmission distance will affect the model's modulation format recognition to a certain extent. As the transmission distance continues to increase, the features of the eye diagram will gradually become blurred, and partial or local distortion may occur.  
Within the test range from 130 km to 180 km, the accuracy of the model can be maintained at more than 80.33%. The dispersion, nonlinear effect, and other fiber transmission characteristics will affect the speed and quality of the signal transmission, which can be compensated by the corresponding digital signal processing algorithms to ensure the integrity and quality of the signal.  
Finally, the effect of different OSNRs on the model MFI was investigated. During the dataset acquisition, the OSNR was varied from 6 dB to 30 dB in steps of 2 dB. The recognition accuracy of the Inception-ResNet-v2 model is shown in Figure 11c. The accuracy of the model can be maintained at 99.68% when the OSNR is varied from 6 dB to 30 dB, and the accuracy of the model can be maintained at 98.29% when the OSNR is varied from 12 dB to 24 dB, and the overall effect is as expected.  
Figure 11: Effect of different factors on model MFI: (**a**) accuracy of the model at different number of rounds; (**b**) effect of different transmission distances on MFI; (**c**) effect of different signal-to-noise ratios on MFI.