# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 3 Radio Modulation Recognition Methods Based on Deep Learning
### Cnn  
CNN is a kind of feedforward neural network with deep structure and large amount of computation. It is one of the representative algorithms of DL [75,76]. CNN has local perception, weight sharing and shift invariance. It exploits spatial local correlation by enforcing local connectivity patterns of adjacent layers, sharing weights between each layer. A basic assumption of CNN is that input data are local and shift invariant. Wireless signal sampling data fit this hypothesis. With the continuous expansion of DL knowledge, the structure of CNN model is more and more diverse. Representative CNN algorithms include LeNet [77], AlexNet [78], ZFNet [79], VGGNet [80], Google LeNet [81], Residual Network (ResNet) [82] and DenseNet [83]. The general architecture of CNN is shown in Figure 1. Typical CNN consists of an input layer, a hidden layer and an output layer. The radio signal x is input into the network from the input layer and data features are extracted and processed through the hidden layer; then the signal modulation mode is output from the output layer and the signals are classified into different modulation modes. The hidden layer includes a convolution layer, an excitation layer, a pooling layer and a fully connected layer (also called dense layer). Some models also use other functional layers in between, such as the normalization layer, the dropout layer and so on. The functions of the main layers are described as follows:  
* Input layer: This layer is used for data entry. \begin{table}
\begin{tabular}{c c} \hline \hline
**DL Model** & **Literature** \\ \hline CNN & [31–56] \\ \hline RNN & [23] \\ \hline LSTM & [24,49,50,57,58] \\ \hline DBN & [21,59–61] \\ \hline \multirow{3}{*}{Other} & GRU [62,63], AE [22,64], CGRN [65], CLDNN [66,67], DenseNet + BLSTM + \\  & DNN [68], CNN + GRU [69,70], CNN + GRU + DNN [71], CNN + LSTM [72,73], \\ \cline{1-1}  & CNN + IndRNN [74] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Applications of DL models in radio modulation recognition. * Convolution layer: This layer uses the convolution kernel for feature extraction and feature mapping [84]. * Activating layer: This layer adds nonlinear mapping by using activation functions, because linear models are not expressive enough. * Pooling layer: This layer carries out a subsampling operation on the feature graph output after convolution, so as to reduce the number of parameters. * Fully connected layer: This layer converts the previous activation graph into a probability distribution and finally sends it to the Softmax layer for classification of categories. * Output layer: This layer is used to output classification results. Radio modulation recognition is no exception in becoming an area of using CNN. There is a lot of literature on radio modulation recognition methods based on the CNN as summarized in Table 2 and described below. [PERSON] et al. [31] used CNN to recognize and classify radio waveforms and used a two-dimensional time-frequency diagram to characterize various signals. When the signal-to-noise ratio (SNR) is \(-2\) dB, the overall ratio of successful recognition (RSR) can reach 93.7%. [PERSON] et al. [32] proposed a signal distortion correction module (CM). They used CM to shift the signal frequency and phase before modulation recognition. Even if CM plus CNN is used for radio modulation recognition, the experimental results show that the recognition accuracy is significantly higher than that of CNN plus CLDNN. There are many modulation recognition methods based on the radar signals. [PERSON] et al. [33] proposed an AMR network for radar signals based on transfer learning CNN. The effective information on the fused image is extracted and identified. When the SNR is \(-6\) dB, the overall RSR can reach 95.5%. [PERSON] et al. [34] combined two CNNs trained on different datasets (i.e., a DL-based combination of two CNNs) to achieve more accurate radio AMR and designed a constellation-based CNN to identify modulation modes that were difficult to distinguish in previous CNNs. [PERSON] et al.