# 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
After reviewing the literature, there are three most commonly used conversion methods: The first is to use the dedicated eye diagram generation module of the oscilloscope to convert the modulated signal into the corresponding eye diagram [39]; the second is to use the eye diagram function in MATLAB to generate the eye diagram; and the third is that researchers need to write their own programs to complete the conversion of the eye diagram. Since the eye diagram itself is generated by the afterglow effect of the oscilloscope, the first method mentioned above will be more accurate when generating the eye diagram. Due to the limitations of experimental conditions, this paper uses the eye diagram function in MATLAB to convert QPSK, 8 PSK, 16 PSK, 4 QAM, 16 QAM, and 256 QAM signals into eye diagrams, as shown in Figure 8.  
The authors of [40] demonstrated preliminary results of deep learning for modulation identification through eye diagrams of signals. The paper first convolves the I and Q eye maps of the signal, secondly connects the I and Q eye maps, then performs maximum pooling, and finally experimentally verifies that the model can achieve a 100% recognition rate for OQPSK as well as BPSK. The recognition rate of 16 QAM is less than 80%. The authors of [41] considered that the original eye diagram did not consider the signal aggregation degree at a specific position, so they enhanced the eye diagram. The authors use the enhanced signal eye diagram as the input to the neural network, and then extract and map features of different dimensions using a multi-input CNN model. Experiments show that the recognition accuracy of the model for BPSK, QPSK, OQPSK, BPSK, and 16 APSK is close to 100% at 2 dB, but the recognition accuracy for 64 QAM, 32 QAM, and 16 QAM is low. Therefore, it is necessary to further improve the intraclass recognition of modulated signal accuracy. [PERSON] et al. [39] proposed a CNN-based optical signal modulation recognition and classification algorithm. The author uses the eye pattern generation module of the oscilloscope to generate four modulated optical signals (return-to-zero on-off keying (RZ-OOK), non-return-to-zero keying (NRZ-OOK), RZ-differential phase shift keying (RZDPSK), and four-pulse amplitude modulation (4 PAM) are converted into eye diagrams, and then CNN is used to learn the features of the eye diagrams and complete the classification. The recognition rate of the four modulation methods is close to 100%. (3) Time-frequency diagram  
If only the characteristics of the signal in the time and frequency domains are analyzed, the characteristics of the signal may be lost. Therefore, to observe the relevant characteristics of the signal more thoroughly, scholars will perform time-frequency analysis of the signal, such as using wavelet transform and other methods, such as converting the signal to a time-frequency map and extracting the features from the time-frequency map. Therefore, it is also a popular method to use deep learning to extract time-frequency map features to complete modulation recognition. We have drawn the time-frequency diagrams of 2 PSK, 4 FSK, 2 PSK, and 4 PSK signals in Figure 9, and we can see that different modulation signals have different time-frequency diagrams. The authors of [42] used neural networks to process time-frequency images of radar signals to identify the modulation types of radar signals. This paper first uses complex wavelet transform to obtain the time-frequency image of the signal, and then uses image cropping, grayscale, adaptive filter normalization, and other steps to enhance the time-frequency image. The results show that when the signal-to-noise ratio is \(-7\) dB, the recognition rate reaches more than 92%, which fully proves that the time-frequency diagram of the signal can well reflect the characteristics of the modulated signal. The author also proposed the Sep-ResNet model for recognition. After comparison, the Sep-ResNet model is better than the ResNet50 and VGG networks.