# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 4 Experiment and Analysis  
### Ablation Experiments  
In the process of building the IQGMCL network, when the input data need to be further processed after features are extracted by the convolution layer, a series of models are established for the number of LSTM layers in the network, the number of fully connected layers, whether to replace LSTM as Bi-LSTM, and whether to introduce multiplicative Gaussian noise, etc. The purpose is to quantitatively determine the network structure for the increase in the number of training features to better complete the recognition task and improve the robustness and recognition accuracy of the model. These networks are represented as IQGMCL-1, IQGMCL-2, IQGMCL-3, IQGMCL-4, IQGMCL-5, and IQGMCL. The specific meaning is shown in Table 2.  
The ablation experiment is conducted on the RML2016.10a dataset. The experiment randomly selects 60% of all data as the training set, the remaining 40% of the data, half as the verification set, and half as the test set. When training, the Epoch is 50, and the Batchsize is set to 128. Using the Adam optimizer based on stochastic gradient descent and the cross-entropy loss function. The initial learning rate is 0.001, and the learning rate is halved for every ten epochs trained. The experimental results are shown in Figure 5. In the figure, enlarging the blue dotted box and the contrast curve of the SNR from 0 dB to 18 dB is plotted separately for intuitive analysis.  
The recognition accuracies obtained by the six networks are visually represented by a histogram, as shown in Figure 6. Macc (The maximum accuracy) in the table represents  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Network Structure** \\ \hline IQGMCL-1 & Convolution + LSTM + LSTM + FC + FC \\ IQGMCL-2 & Convolution + LSTM + LSTM + FC \\ IQGMCL-3 & Convolution + LSTM + LSTM \\ IQGMCL-4 & Convolution + LSTM + FC \\ IQGMCL-5 & Convolution + LSTM + Bi-LSTM + FC \\ IQGMCL & Convolution + LSTM + Bi-LSTM + FC + Gaussian noise \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison table of network structure of each model.  
Figure 5: Comparison chart of each improvement network experiment.  
the maximum recognition accuracy of the networks in the SNR range. Aacc (The average accuracy) represents the average recognition accuracy of the networks when the SNR is greater than 0 dB.  
Figures 5 and 6 show the IQGMCL network has better recognition accuracy than the comparison networks. Comparing the two networks IQGMCL-1 and IQGMCL-2, the IQGMCL-2 recognition effect is better. Because of the introduction of signal-relevant features, the number of features increases. The model appears overfitting. Reducing a layer of FC can alleviate overfitting. Compared with IQGMCL-1, Macc and Aacc are increased by 0.41% and 0.4%, respectively. Comparing the three models IQGMCL-2, IQGMCL-3, and IQGMCL-4. IQGMCL-3 and IQGMCL-4 are based on IQGMCL-2 to remove the FC layer and a layer of LSTM, respectively. Both results are not as good as IQGMCL-2. The comparison of IQGMCL-2 and IQGMCL-5 shows that when Bi-LSTM is replaced on the basis of IQGMCL-2, the recognition effect of the IQGMCL-5 model will be improved. Compared with IQGMCL-2, IQGMCL-5 model's Macc and Aacc are increased by 0.39% and 0.1%, respectively, because Bi-LSTM can better explore data features when the input features of the model increase, but IQGMCL-5 still has an overfitting phenomenon in the training process. To further alleviate overfitting and improve model accuracy, on this basis, the multiplicative Gaussian noise regularization layer is added. The mean of the noise level is set to 1, and the standard deviation is set to 0.5. The layer can only be mobilized when the network is trained. This layer can further eliminate the isolated features in training and improve the model's generalization ability. Experiments show that the IQGMCL network has a better recognition effect. Compared with IQGMCL-5, Macc and Aacc are increased by 0.47% and 0.6%, respectively.