# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 5 Radio Modulation Recognition Model Based on CNN
### Modulation Recognition of Radio Signals by CNN
The fourth Conv/ReLU layer has the number of output filters 80 and the filter size \(2\times 3\). These four layers are ended with dropout for regularization. The four convolution layers are followed by two dense (fully connected) layers. The first dense layer, after the input is flattened, obtains its output by taking the dot product between input tensor and 256 kernel matrices and then the activation function ReLU is used. The second dense layer obtains its output by taking the dot product between input tensor and an 11 kernel matrix and then Softmax is used as activation function. Since  
Figure 6: Flow chart of CNN-based AMR method. Figure 7: Implementation of our method. at this stage we want to classify the types of signals to be recognized, the output here is \(11\) classes, corresponding to \(11\) types of different signals in the dataset. After running, we can obtain the results described below. In order to verify the effectiveness of the proposed model, existing recognition methods such as CNN, ResNet, Inception and CLDNN are compared with the methods in this Section. The parameters of the existing recognition methods are kept in the original parameter setting of the method. After training on the RadioML2016.04C dataset, the experimental results are shown in Figure 9. This figure shows the recognition accuracy of each method with SNR range of [\(-20\) dB, \(+18\) dB], in which the blue line represents the recognition accuracy of the method in this section. It can be seen that the recognition accuracy of the proposed model in the range of [\(-20\) dB, \(-10\) dB] is not much different from those of the existing methods, but the recognition accuracy of the proposed model in the range of [\(-10\) dB, \(18\) dB] is better than those of the other methods. The highest recognition accuracy is \(98.47\%\) at \(+18\) dB. It is verified that our method is effective for wireless signal modulation recognition task. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & **Size of Convolution** & **Layer Output** & **Layer Activation** & **Number of Trainable** \\  & **Kernel** & **Dimension** & **Function** & **Parameters** \\ \hline Input layer & - & \(2\times 128\) & - & 0 \\ \hline Pooled convolutional & \(1\times 3\) & \(130\times 256\) & ReLU & 1024 \\ layer 1 & \(1\times 3\) & \(132\times 256\) & ReLU & 196,864 \\ \hline Pooled convolutional & \(1\times 3\) & \(134\times 256\) & ReLU & 196,864 \\ layer 3 & \(1\times 3\) & \(134\times 256\) & ReLU & 196,864 \\ \hline Pooled convolutional & \(2\times 3\) & \(136\times 80\) & ReLU & 122,960 \\ layer 4 & \(2\times 3\) & \(136\times 80\) & ReLU & 2,785,536 \\ \hline Full connected layer 1 & - & \(1\times 256\) & ReLU & 2827 \\ \hline Full connected layer 2 & - & \(1\times 11\) & Softmax & 2827 \\ \hline Output layer & - & \(1\times 11\) & - & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Detailed structure of the model. Figure 8: Modulation recognition model based on CNN. Table 12 shows the comparison of simulation results of these five methods. From the table, we can see that the method in this paper has a relatively small number of parameters. The recognition accuracy and average recognition accuracy are the highest. The performance of AMR is superior to the other four methods. Figure 10 shows the confusion matrices of the above five methods when the SNR is 18 dB. Through comparison, it can be found that the confusion matrix of our proposed method has almost no light color blocks and the diagonal color blocks are darker. This further shows that our method has almost no misclassification and most modulation types can be accurately recognized. We set epoch to 100 and batch to 1024. Cross entropy loss function and Adam optimizer are used. At the same time, the early stop mechanism is used. The patience is set to 5, which means that the loss function of five consecutive batches has not changed and the training will be stopped automatically, so as to prevent overfitting.