# Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition
## 3 AI-Based Modulation Format Recognition
### Results and Discussion
#### 3.2.3 Comparison with Different Methods  
To further validate the performance of the Inception-ResNet-v2 model, it was compared with a total of six deep learning methods--AlexNet, ResNet50, GoogleNet, SqueezeNet, Inception-v4, and Xception--with the same simulation conditions, and the comparison results are shown in Figure 12, the different colors in the figure are used to distinguish between six different deep learning methods, with the specific method names indicated on the horizontal axis. The selected six deep learning methods are highly regarded and widely adopted in the field of image recognition and classification. They represent neural network architectures from different developmental stages and with different design philosophies, each having unique characteristics and applicability to various scenarios; researchers often use these methods for modulation format identification [15; 16; 17; 18; 19; 20], which are representative to some extent. To ensure fairness and accuracy in the comparison, all participating methods utilized a unified dataset. This dataset comprehensively covers optical signals under multiple modulation formats and extensively includes various optical signal-to-noise ratio (OSNR) ranges and transmission distances, aiming to simulate the complex and varied actual operating environments in optical communication systems. To objectively assess the generalization ability of each model, a random split strategy was adopted to divide the dataset into two parts, forming a training set and a test set with independent splits each time, and the average accuracy was calculated to mitigate the potential interference of randomness on the experimental results.  
As shown in Figure 12a, the accuracy of all six neural network architectures exceeded 95%, indicating a strong recognition capability across these models. Notably, the accuracy of the SqueezeNet model reached 97.89%, while the accuracy of the Inception-ResNet-v2 model was even higher at 99.47%, representing an improvement of 1.58% over the  
Figure 12: Comparative analysis of different modulation format recognition methods: (**a**) accuracy; (**b**) precision; (**c**) recall; (**d**) F1 score.  
SqueezeNet model, which highlights the significant advantage of the Inception-ResNet-v2 model in terms of accuracy.  
From Figure 12b, the precision of the Inception-ResNet-v2 model reached 98.75%, which is 10.79% higher than the lowest precision of the AlexNet model. High precision means that when the model predicts a sample to belong to a certain category, the probability of this prediction being correct is high, thus the Inception-ResNet-v2 model performs excellently in reducing false positives and has high reliability.  
Figure 12c shows that the recall rate of the Inception-ResNet-v2 model was 98.88%, meaning that it effectively identified almost all samples that belonged to the positive class (i.e., specific modulation formats). A high recall rate is crucial for avoiding missed detections, especially in application scenarios requiring comprehensive detection of specific modulation formats.  
Finally, Figure 12d reveals that the F1 score of the Inception-ResNet-v2 model was 97.54%, which is a composite evaluation metric combining precision and recall. The high F1 score further demonstrates that the Inception-ResNet-v2 model not only maintains high precision but also sustains a high level of recall, thereby showcasing more balanced and efficient recognition capabilities in practical applications.