# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 5 Radio Modulation Recognition Model Based on CNN
### Influence of CNN Network Hyperparameters on Modulation Recognition Rate
#### 5.2.2 Influence of the Number of Convolution Kernels on Recognition Results  
We still use the model shown in Figure 8. The number of convolution kernels in the 1 st, 2 nd and 3 rd convolution layer is changed from 256 to 128 and 64, respectively. Comparison of the recognition accuracy of different SNRs is shown in Figure 12. We can see that the number of convolution kernels has little effect on the recognition rate at low SNR (SNR less than \(-4\) dB). However, when SNR is higher than \(-4\) dB, we can observe that the recognition rate increases with the increase of convolution kernel. The recognition rate of 256 convolution kernels is the highest. This shows that the more convolution kernels, the stronger the fitting ability and the higher the recognition rate. At the same time, we also observe that the recognition rate of 256 convolution kernels is a little bit higher than that of the other two. This shows that the fitting ability of convolution training dataset is also limited. When the convolution kernel number increases to a certain extent, the feature extraction ability will not change much. The corresponding recognition rate is similar. Therefore, it is very important to choose the number of convolution kernels when recognizing radio signals. Common Evaluation Parameters  
Confusion matrix, accuracy rate, recall rate and average precision are commonly used parameters in DL. We can judge the performance of a network model according to them. That is to say, when we observe the training results, we pay attention to the changes in these parameters. Therefore, it is necessary to observe these evaluation parameters when using DL to recognize the modulation of radio signals. Figure 11: Recognition accuracy of different number of convolution layers. Figure 12: Recognition accuracy of different number of convolution kernels. First of all, the most important parameter is the confusion matrix, from which we can clearly see the prediction and the actual situation. Let us take the classification problem as an example. The binary confusion matrix is shown in Table 13, where '1' is a positive class and '0' is a negative class and 'Predicted' stands for predicted results and 'Actual' stands for actual results. Other variables in the table are defined as  
* _TP_: The true value is positive and the predicted value of the model is positive (True Positive = _TP_)
* _FN_: The actual value is positive and the predicted value of the model is negative (False Negative = _FN_)
* _FP_: The actual value is negative and the predicted value of the model is positive (False Positive = _FP_)
* _TN_: The true value is negative and the predicted value of the model is negative (True Negative = _TN_)  
Suppose we want to classify radio signals containing only QPSK and WBFM modulation modes. Assume that in the confusion matrix Table 13, '1' represents QPSK and '0' represents WBFM. Then, _TP_ represents the number of QPSK correctly predicted. _FN_ represents the amount of QPSK that has been mispredicted as WBFM. _TN_ represents the number of WBFM correctly predicted. _FP_ represents the number of WBFM mispredicted as QPSK. _TP + FN_ represents the actual amount of QPSK. _FP + TN_ represents the actual amount of WBFM. _TP + FP_ represents the amount predicted to be QPSK. _FN + TN_ represents the amount predicted to be WBFM. _TP + TN + FP + FN_ represents the total number of WBFM and QPSK samples. Therefore, we can clearly see the actual and predicted results from the confusion matrix. For a given set of test data, accuracy is defined as the ratio of the number of samples correctly classified by the classifier to the total number of samples, which is expressed as below  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
The accuracy rate is generally used to evaluate the global accuracy of a model. It cannot contain too much information to comprehensively evaluate the performance of a model. In short, it is the proportion of the number of correctly predicted samples to the total number of samples.