## 4 Results
### Qualitative Analysis of Model Interpretability
#### 4.2.2 Interpretability of Models on the Constellation Phase Histogram Dataset  
Similarly, we evaluated the interpretability of the AMI-CNN model on the constellation phase histogram dataset, comparing it with current mainstream interpretability methods, including Grad-CAM, LIME, and SHAP. Figure 6: Interpretability results of QPSK on the PSD dataset (the closer the region is to the red color, the greater the weight, representing the more attention the model pays to the feature). In the constellation diagram, the features of different modulation formats are the distinct clustering positions of data points. In our experiment, we used the phase histogram as the raw data input. The interpretable methods generated a 1024-length one-dimensional sequence representing the weight of each phase interval. The higher that weights mean the greater the contribution of that phase feature to the model's decision-making process. In Figure 7, we restored the weights on the phases and encircled the original constellation diagram to represent the weight of each phase feature. The model exhibits better interpretability when the high-weight phase features marked by the model coincide with the theoretical phase features at the clustering points, i.e., when the phases marked with colors closer to white overlap more with the clustering positions. Figure 7 presents the interpretability results of the four methods across three modulation formats. For QPSK, the theoretical clustering points of QPSK phases concentrate at 45\({}^{\circ}\), 135\({}^{\circ}\), 225\({}^{\circ}\), and 315\({}^{\circ}\). It can be observed that both AMI-CNN and Grad-CAM highlight regions that are prominent and align with the theoretical clustering points, demonstrating good interpretability. While the highlighted phases by SHAP are not as conspicuous as those by  
Figure 7: Interpretability results of QPSK on the constellation phase histogram dataset (the closer the region is to the white color, the greater the weight, representing the more attention the model pays to the feature). the former two methods, they still correspond to the phase positions of the clustering points. However, the annotations by LIME appear more scattered, with the main highlighted features deviating from the theoretical clustering points. For 8 PSK, the theoretical clustering points of 8 PSK phases concentrate at 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\), 225\({}^{\circ}\), 270\({}^{\circ}\), and 315\({}^{\circ}\). The AMI-CNN model presents a clear interpretability result, with each annotated phase overlapping the clustering phases. Furthermore, the detailed trend of variation for each annotated block is evident. Taking the annotated phases at 0\({}^{\circ}\) as an example, with 0\({}^{\circ}\) as the center, the colors are closest to white, indicating high weights, and as the phase deviates further from 0\({}^{\circ}\), the color gradually approaches black, indicating low weights. This color variation cleverly reflects the trend where there are more data points near the center of each clustering point in the constellation diagram and fewer data points toward the periphery. LIME still exhibits scattered annotations. In contrast, Grad-CAM and SHAP annotations also almost perfectly overlap with the phase positions of the clustering points, but they demonstrate distinct characteristics in the attention areas. Specifically, Grad-CAM's attention areas tend to be more blocky and lack detail. Conversely, SHAP focuses more on local details, but it also does not highlight detailed features as accurately as AMI-CNN. For 16 QAM, the theoretical clustering points of 16 QAM phases concentrate at 22.5\({}^{\circ}\), 45\({}^{\circ}\), 67.5\({}^{\circ}\), 112.5\({}^{\circ}\), 135\({}^{\circ}\), 157.5\({}^{\circ}\), 202.5\({}^{\circ}\), 225\({}^{\circ}\), 247.5\({}^{\circ}\), 292.5\({}^{\circ}\), 315\({}^{\circ}\), and 337.5\({}^{\circ}\).