# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 4 Experiment and Analysis
### Comparative Experiments of Different Networks
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Macc/Aacc** & **RML2016.10a** & **RML2016.10b** & **RML2016.04c** \\ \hline IQGMCL & 0.9352/0.923 & 0.9374/0.933 & 0.9942/0.975 \\ \hline MCLDNN & 0.9221/0.911 & 0.9363/0.930 & 0.9845/0.965 \\ \hline LSTM2 & 0.9204/0.908 & 0.9361/0.925 & 0.9502/0.905 \\ \hline PETCGDNN & 0.9163/0.902 & 0.9322/0.926 & 0.9787/0.944 \\ \hline GRU2 & 0.9136/0.902 & 0.9335/0.923 & 0.9320/0.881 \\ \hline CLDNN & 0.8472/0.835 & 0.9205/0.914 & 0.9322/0.918 \\ \hline ICAMNET & 0.8377/0.821 & 0.9286/0.917 & 0.9803/0.955 \\ \hline CNN1 & 0.8186/0.803 & 0.8476/0.842 & 0.9417/0.925 \\ \hline CNN2 & 0.8345/0.822 & 0.8690/0.858 & 0.9793/0.941 \\ \hline ResNet & 0.8177/0.803 & 0.9081/0.896 & 0.9878/0.959 \\ \hline DenseNet & 0.7891/0.769 & 0.9071/0.896 & 0.9803/0.939 \\ \hline CGDNet & 0.8172/0.795 & 0.8971/0.889 & 0.9529/0.918 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Macc and Aacc for each network. two figures on three different datasets. Among the comparison networks, it can be found that the MCLDNN network has the best recognition accuracy. However, the IQGMCL network proposed in this paper has a higher recognition accuracy than MCLDNN. Therefore, MCLDNN is used as a reference object to present the results of further experiments. According to Table 3, on the RML2016.10a dataset, the Macc and the Aacc of the IQGMCL network are 93.52% and 92.3%, respectively, which are 1.31% and 1.2% higher than the MCLDNN network. On the RML2016.10b and RML2016.04c datasets, the Macc and the Aacc of the IQGMCL network are 93.72%, 93.3%, 99.42%, and 97.5%, respectively, which are improved by 0.11%, 0.3%, 0.97%, and 1% compared with the MCLDNN network. The network weights will be continuously updated in real time during the training process. We supervise val-accuracy to save the weights with the best verification accuracy for testing. Using the original spatiotemporal multi-channel network MCLDNN as the comparison model, the best verification accuracy and loss function of the IQGMCL network in the training process on three datasets are shown in Figure 9 below. As can be seen from Figure 9a,b, the improved spatiotemporal multi-channel network on the three datasets is better than the original, both in the loss function and the verification accuracy. In the specific training process, the comparison curves of training accuracy (train_accuracy), training loss (train_loss), verification accuracy (val_accuracy), and validation loss (val_loss) of the IQGMCL network and MCLDNN network are shown in Figure 10.  
From Figure 10a,b, it can be found that the train_accuracy and train_loss of the IQGMCL network are significantly better than those of the MCLDNN network on the three publicly available datasets. This verifies the conclusion that the training accuracy is improved due to the increase in the number of features after the introduction of the relevant features of the IQ signal. From Figure 10c, it can be concluded that on each dataset, the val_accuracy of the IQGMCL network is better than that of the MCLDNN network as a whole. Especially on the RML2016.10a dataset, the IQGMCL network can achieve better recognition with fewer training times. After 20 epochs, the IQGMCL's val_accuracy has reached a good level, indicating that the network converges faster, and IQGMCL's highest val_accuracy is higher than the original MCLDNN network. As can be seen from Figure 10d, on RML2016.10a, the IQGMCL network can achieve a lower level of val_loss with fewer training epochs. It also verifies that the network converges faster and shows that the val_loss is lower. On RML2016.10b and RML2016.04c, the IQGMCL's val_loss is smaller than that of the MCLDNN network in general. Figure 9: Val-accuracy and Val-loss contrast of two networks. (**a**) Val-accuracy contrast; (**b**) Val-loss contrast.