## 4 Results and Discussion
### Ablation Study  
The goal of an ablation study is to investigate how modifying or replacing network components and the hyperparameters affects the overall performance. We performed five groups of ablation studies, respectively, as mentioned in Table 3.  
For each configuration, the model was trained and tested 10 times, and the one yielding the best performance in terms of accuracy was selected as the final model. The performance comparison of several configurations is shown in Figure 9, indicating that the RS-proposed network has better classification accuracy than its counterparts. In the process of building the residual learning and squeeze-and-excitation network (RS), a range of models are developed for different aspects, such as number of RS blocks, the number of fully connected layers, the replacement of the max pooling layer, and replacing the number of filters and kernel sizes. These models are used to further process the input data after the features have been extracted by the convolution layer. The objective is to analyze and determine the optimal network structure that can effectively utilize a larger number of training features to enhance the classification accuracy and robustness of the model. These networks are represented as RS-1, RS-2, RS-3, RS-4, and RS-proposed. In Table 3, the configuration is illustrated. \begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Network Structure** \\ \hline RS-1 & RS, RS, RS, FC \\ \hline RS-2 & RS, RS, RS, RS, RS, FC, FC \\ \hline RS-3 & RS, RS, RS, FC, FC, kernel size and filter replaced \\ \hline RS-4 & RS, global average pooling 2D, RS, RS, RS, FC, FC \\ \hline RS-Proposed & RS,RS, RS, RS, max pooling 2D, FC, FC \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Network structures of several models included in ablation study. Figure 8: Classification accuracy of the various traditional feature-based methods on _D2_. A random selection of 70% of the total data is used as the training set, with 30% used as the verification set. The batch size is set to 512 and the epoch is 50 when training. Utilizing the categorical cross-entropy loss function and the Adam optimizer, the learning rate is set to 0.0001. In Table 4, the experimental findings are mentioned. The proposed network has the best findings, as illustrated in Table 4. When compared to other ablation tests, the proposed approach has the best classification performance. This indicates that the diverse benefits of the different components of the residual learning and squeeze-and-excitation blocks (RS blocks) complement each other, and their combination leads to exceptional performance. In particular, the proposed network and RS-3 network perform better than the RS-4 network in classification performance, indicating that rich temporal information for modulation classification may be extracted by the layers of temporal feature extraction. The addition of signal-relevant features raises the overall number of features. The model appears to be overfitting. Overfitting can be reduced by reducing a layer of FC. Furthermore, the comparison of RS-3 with RS-proposed reveals that designing lightweight networks with multiple RS blocks and the replacement of different numbers of parameters and kernel sizes can be a meaningful approach to reduce model complexity. This insight can help in developing more efficient models. By comparing these models, RS1 outperforms RS2, having few layers and one FC layer. On the other hand, RS-3 and RS-4 are based on RS-2, which adds the various sizes of filters and kernels by changing pooling layers, respectively. The comparison of RS-3 and RS-2 showed that alternating the size of the filter and kernel sizes with two FC layers can yield better classification accuracy than RS-2, which has only six RS layers and two FC layers. In RS-4, the max pool 2D is replaced by global average pooling. It takes the average of the feature map, and the resulting vector is fed directly into the activation layer. This layer yields low classification from other layers due to overfitting.