## 4 Results  
### Baseline Model Selection  
For the study of interpretability, mainstream methods typically combine trained models with a series of interpretability methods (e.g., Grad-CAM, LIME, SHAP). Therefore, for each dataset, we need to select the appropriate models for combination.  
To compare the fundamental performances of the models, we conducted measurements on accuracy and model parameter count for both the PSD and constellation phase histogram datasets, as shown in Tables 2 and 3, respectively. For the comparative experiments, we selected three excellent deep learning models: LeNet, ResNet18, and VGG19.  
From Table 2, it is evident that all three models achieved 100% classification accuracy on the PSD dataset. However, in terms of model parameter count, LeNet had significantly fewer parameters than the other three models.  
For the constellation phase histogram dataset, Table 3 displays the accuracy and parameter count of different models. The results indicate that the accuracy of LeNet is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Type** & **LeNet** & **ResNet18** & **VGG19** \\ \hline Accuracy & 100\% & 100\% & 100\% \\ \hline Total Parameters & \(7.19\times 10^{5}\) & \(8.7\times 10^{6}\) & \(1.19\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of parameters and accuracy on the PSD dataset for LeNet, ResNet18, and VGG19.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Type** & **LeNet** & **ResNet18** & **VGG19** \\ \hline Accuracy & 100\% & 100\% & 100\% \\ \hline Total Parameters & \(7.19\times 10^{5}\) & \(8.73\times 10^{6}\) & \(1.19\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of parameters and accuracy on the constellation phase histogram dataset for LeNet, ResNet18, and VGG19.  
comparable with those of other models, while also exhibiting significant advantages in terms of parameter count.  
Additionally, we examined the time required for the models to achieve 100% accuracy. Considering the GPU startup time and other potential interferences, we ran each model ten times and observed that the time performance was unstable for the first three runs. However, from the fourth run onward, the time stabilized. Therefore, we averaged the last seven runs out of ten to obtain the average runtime for each model, and the results are recorded in Figure 5. We noted that LeNet required the least time on both datasets. The runtime of the VGG19 model was significantly longer than those of the other three models, likely due to its high complexity and large number of parameters. On the PSD dataset, LeNet required 0.074 s and 14.137 s less time to achieve 100% accuracy compared with ResNet18 and VGG19, respectively. On the constellation phase histogram dataset, LeNet required 0.495 s and 6.193 s less time than ResNet18 and VGG19, respectively, to achieve 100% accuracy.  
Therefore, for the PSD dataset, we chose the LeNet model, which has the fewest parameters and the fastest convergence among the three comparison models. For the constellation phase histogram dataset, due to the sparsity of the data, meaning a large number of zero values are present in the data, we abandoned the LeNet model, which is prone to getting stuck in local optima and difficult to converge. Instead, we chose the ResNet18 model, which has a similar computation time to the LeNet model but incorporates a residual structure. The residual structure in ResNet18 effectively prevents gradient vanishing and exploding problems.