# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 4 Experiment and Analysis
### Comparative Experiments of Different Networks  
In Section 4.1, the specific structure of the IQGMCL network is established through comparative experiments. In this section, the network is compared with the existing advanced modulated signal recognition networks, namely CNN1, CNN2, CLDNN, DenseNet, ResNet, CGDNet, GRU2 [36], ICAMCNET, LSTM2 [37], PETCGDNN, and MCLDNN. Three open-source datasets are compared: RML2016.10a, RML2016.10b, and RML2016.04c. Similarly, 60% of all data randomly selected on each dataset is the training set, 20% is the validation set, and 20% is the test set. When training, the Epoch is 40, 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. Network training in experiments is supervised learning. Each  
Figure 6: Various network experiments Macc and Aacc. (**a**) Macc; (**b**) Aacc. sample signal has a data label corresponding to the category name of each signal. The label adopts a one-hot encoding method. The construction of neural networks and the training of models use Tensorflow version 2.6.0 python 3.8. Use the model.fit(0) function for training, and pass in parameters, including training data and labels, validation data and labels, Batchsize, Epoch, and so on. The experimental results obtained are shown in Figure 7.  
It can be seen from Figure 7a that on the RML2016.10b dataset, the recognition effect of the IQGMCL network and MCLDNN network is not much different when the SNR is high, and the IQGMCL network is slightly better. However, when the SNRs are between \(-10\) dB and \(6\) dB, the IQGMCL network has obvious advantages. Figure 7b shows that compared with the existing modulation recognition network, the proposed improved network has a better recognition effect when the SNR is greater than \(0\) dB on the RML2016.04c dataset. From the experimental results of Figure 7c, it can be seen that on the RML2016.10a dataset,  
Figure 7: Comparison results on three datasets. (**a**) Comparison results of RML2016.10b dataset; (**b**) comparison results of RML2016.04c dataset; (**c**) comparison results of RML2016.10a dataset. the IQGMCL network shows better recognition accuracy than other networks when the SNR is greater than \(-2\) dB. The highest recognition accuracy of each network in the SNR range and the average recognition accuracy when the SNR is greater than \(0\) dB are shown in Table 3 below. Macc (The maximum accuracy) in the table represents 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. Figure 8a shows the highest signal recognition accuracy of multiple networks in the SNR range on three public datasets. Figure 8b shows the average signal recognition accuracy of multiple networks when the SNR is greater than \(0\) dB on three public datasets. The recognition effect of each network model can be compared more  
Figure 8: Macc and Aacc of each network on three datasets. (**a**) Comparison of the maximum recognition accuracy of each network; (**b**) comparison of the average recognition accuracy of each network.