# 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  
1. A two-dimensional image recognition method based on CNN. Converting the signal to a 2D image and then using a CNN to identify the modulation is very popular, so we will introduce this method in this section. 1. Constellation map  
A constellation diagram is a graphical insight into the projection of a signal into an orthogonal vector space, the dimensionality of which is determined by the specific type of modulation. Two-dimensional constellation diagrams are by far the most common, which can reflect different characteristics of different modulation types. This paper shows the constellation diagram of the 8 PSK signal at 20 dB, 15 dB, 5 dB, and 0 dB in Figure 7. It can be seen from Figure 7 that the features of the constellation diagram are more obvious under high signal-to-noise ratio and less obvious under low signal-to-noise ratio. Therefore, some scholars will convert the single-channel constellation diagram into a three-channel color constellation diagram to increase the recognition rate, which can achieve better results, so constellation diagrams are often used in modulation identification. [PERSON] et al. [36] converted complex signals into constellation maps and applied two popular CNN models (AlexNet and GoogleNet) to the recognition of complex signals and designed a CNN-based modulation recognition algorithm. Finally, experiments showed that when the signal-to-noise ratio is greater than 6 dB, the recognition rate of the algorithm for eight kinds of modulation signals reaches more than 95%, which is better than the traditional support vector machine. However, when the signal-to-noise ratio is  
Figure 7: Constellation diagram of 8 PSK under different signal-to-noise ratios. less than 1 dB, the recognition accuracy of the model is less than 80%. It can be seen that the recognition accuracy under low signal-to-noise ratio needs to be improved. In order to improve the recognition classification accuracy, [PERSON] [37] processed the constellation map into a heat map with colored shadows. Then, the typical CNN model is used, namely VGG16, VGG19, InceptionV3, Xception, and ResNet50 to identify the constellation heatmaps of six modulation methods. Among them, ResNet50 has the best classification accuracy, and the accuracy rate can reach more than 95%. However, when the signal-to-noise ratio drops to 2 dB, the recognition accuracy drops to 80%, so this method is only suitable for high signal-to-noise ratio environments. Due to the lack of relevant research on DL-based AMR in MIMO-OFDM systems, the authors of [38] proposed a series of constellation multimodal feature networks (SC-MFNet) for the modulation recognition problem of MIMO-OFDM systems in their work. The SC-MFNet network has four parts, including the feature extraction module based on Conv1 DNet, the constellation feature extraction module based on efficient network, the multimodal feature fusion module, and the full connection classifier. The authors input the waveform diagrams of the five modulated signals (BPSK, QPSK, 8 PSK, 16 QAM, and 32 QAM) and the segmented accumulated constellation diagrams into the SC-MFNet network, and the network extracts the features of the signal waveform diagrams and constellation diagrams and fuses the features. Final classification experiments show that the SC-MFNet has a recognition accuracy of 95% when the signal-to-noise ratio is 0 dB. (2) Eye diagram  
The eye diagram is a waveform displayed by a series of digital signals accumulated on the oscilloscope that can reflect the overall characteristics of the digital signal. The eye diagram characteristics of different modulation signals are different, so the signal can be converted into an eye diagram, and then the deep learning network model is used to extract the features in the eye diagram to complete modulation recognition. Converting the modulation signal into an eye diagram is an important step in the modulation identification process.