# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 3 Radio Modulation Recognition Methods Based on Deep Learning
### Cnn
[35] proposed a method of radio automatic modulation recognition based on CNN, which used short-time Fourier transform (STFT) to create spectrogram images of different complex signals to convert complex modulation recognition problems into image recognition problems. [PERSON] et al. [36] proposed radio access technology (RAT), a wireless access technology based on the combination of time-frequency distribution and CNN. Time-frequency analysis was used to obtain the spectral content of the signal. CNN was used for feature extraction and recognition. Performance charts and confusion matrices for correct recognition were used to analyze the accuracy of the network. [PERSON] et al. [37] proposed a radio AMC method with multifeature fusion based on CNN, which can achieve the same or better results with less learning parameters and training time. [PERSON] et al. [38] proposed a BCI-assisted generalized AMR based on DL of two CNNs: the former identifies the channel category of the signal, while the latter classifies the signals under the same channel. The simulation shows that the proposed GenAMR method is significantly better than the traditional one. [PERSON] et al. [39] studied three fusion methods when the signal length is longer than the designed CNN input length: voting-based fusion, trust-based fusion and feature-based fusion. Experiments show that the latter two methods have better performance. Yong  
Figure 1: Typical CNN structure. [PERSON] et al. [40] proposed a radio AMC method based on wavelet denoising pre-processing and improved CNN architecture. The method first decomposes the baseband signal into various frequency scales by wavelet transform, then classifies the pre-processed signal by improved CNN. [PERSON] et al. [41] proposed an AMC modulation classification based on dense layer DropoutCNN (DDrCNN), selected the classification cross-entropy as the loss function and selected Adam as the optimization function, including only one CNN. The modulation schemes are classified by the IQ samples of training data. Above 97% accuracy can be achieved over 2 dB SNR. [PERSON] et al. [42] proposed a sparse filtering criterion to carry out unsupervised layer-by-layer pre-training of the CNN network, which effectively improved the generalization ability. [PERSON] et al. [43] used AlexNet and GoogleNet, two DL models based on CNN, to classify the signals for modulation. That is, several methods have been developed to represent the modulated signals in the data format of CNN with a grid topology. [PERSON] et al. [44] constructed a five-layer CNN model to identify VHF signals. Simulation and actual signals showed that the influence of frequency shift and noise on accuracy was great. [PERSON] et al. [45] proposed the idea of using CNN to classify the modulation types in the communication system. In their method, the constellation diagram was used to represent the modulated signal of CNN. AlexNet model was used for training and testing. [PERSON] et al. [46] used time-domain features, such as IQ vector and amplitude/phase vector, to train the CNN classifier. Experimental results showed that the scheme could identify ZigBee, WiFi and Bluetooth signals well. [PERSON] et al. [47] extended the deep CNN model of radio and used deep residual network for signal classification. Its robustness was also discussed. [PERSON] et al. [48] proposed a supervision model based on CNN and trained a series of pseudo labeled time slice spectral data in the model. [PERSON] et al. [49] proposed an AMR framework based on CNN. A preprocessed signal representation was proposed, which combined the orthogonal, fourth-order statistics of the modulated signals. The accuracy was improved by 8%. [PERSON] et al. [50] proposed an AMR method for radio signals based on improved CNN. The classification accuracy can reach 93%. [PERSON] et al. [51] proposed a lightweight CNN for AMC. Different model blocks in the network were used for feature extraction, feature reconstruction and full connection classification.