# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 3 The Modulation Recognition Method Based on Deep Learning
### Application of Convolutional Neural Network in Modulation Recognition
#### 3.1.2 The Modulation Recognition Method Based on Convolutional Neural Network
The authors of [43] proposed an LPI radar signal recognition method based on dual-channel CNN and feature fusion. The authors used the wavelet transform method to convert the signal into a time-frequency map, and the time-frequency map was processed in grayscale. Subsequently, it was inputted into the two-channel CNN model, which can extract two features, the oriented gradient (HOG) and the depth feature histogram, from  
Figure 8: Eye diagram of modulated signals. the signal time-frequency map and finally fuse the two features and classify them. The classification method has a signal-to-noise ratio of 6 dB, and the recognition rate can reach more than 95%. The authors of [44] proposed a modulation and identification method of impulse noise communication signals based on fractional low-order Choi-Williams distribution and CNN, aiming at the low recognition rate of a communication signal in non-Gaussian noise. Feature extraction was performed, and then FLO-CWD used to transform the signal time-frequency map by inputting the transformed time-frequency map into the improved CNN for the second feature extraction and classification. The recognition rate of this method reaches 95% at 4 dB. However, this method only recognizes signals of 2 ASK, 2 FSK, and 2 PSK modulation methods and does not know the recognition rate of other modulation methods. Therefore, if we want to apply this method to actual communication systems, we need to continue research and optimization. (4) Circulation spectrogram  
A cyclic spectrum has good anti-noise performance, so it is often used to analyze signals in environments with large noise interference. The 3D graph output by the cyclic spectrum can give an intuitive impression, and the signal can be further analyzed by the cross-sectional view of the 3D graph in different directions. In order to further visualize the cyclic spectrogram of the modulated signal, this paper uses MATLAB to draw the three-dimensional cyclic spectrogram of QPSK and 4 ASK and intercept the two-dimensional part when the cyclic frequency alpha is equal to 0, as shown in Figure 10.  
In 2019, the author of [45] made improvements to address the poor performance of constellation maps at low signal-to-noise ratios, resulting in low recognition rates and poor adaptation phases, and proposed a modulation recognition method based on innovative CNNs and recurrent spectrograms. The author first maps the three-dimensional cyclic spectrum of the signal into two dimensions, and then takes the two-dimensional cyclic spectrum as the dataset. Then the improved CNN is used to extract the features  
Figure 9: Time-frequency diagram of modulation signal. of the cyclic spectrum. Finally, the softmax layer is used to successfully identify eight modulation modes. The authors of [46] proposed using a deep learning algorithm to process the cyclic spectrogram of the signal to identify the secondary modulation signal. The author uses AlexNet, vgg16, vgg19, and resnet18 to recognize the two-dimensional cyclic spectrum images of seven modulated signals (BPSK, QPSK, 2 FSK, bpsk-pm, qpsk-pm, 2 fsk-pm, and DS-BPSK). The experimental results show that the recognition accuracy of VGG19 and ResNet18 is better, but the confusion rate of BPSK-PM and BPSK-PM is high. (5) Amplitude Histogram  
The amplitude histogram of the signal can represent the relationship between the amplitude of the signal and the number of sampling points of different amplitudes. The amplitude histograms of different modulation methods have large differences, as shown in Figure 11, which shows that the amplitude histograms of different modulation methods are all different shapes. The authors [47], proposed a new scheme of CNN. Joint OSNR monitoring and MFI based on the signal amplitude histogram.