# 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 author uses the constant modulus algorithm (CMA) to obtain modulated signal samples after equalization to draw amplitude histograms (Ahs), and then uses the convolutional neural network a design based upon VGG to extract the features of the amplitude histograms for classification and identification. After 2500 times of training, the recognition rate of QPSK, 8-QAM, and 16 QAM reached 100%, indicating that CNN can identify the modulation type of the signal according to the histogram of the signal. The authors [48] proposed a multi-layer neural network modulation pattern recognition method based on cyclic cumulants of communication signals. The authors use the improved CNN to extract the signal features represented in the cyclic spectrograms of MPSK and MQAM, and then use the softmax layer to complete the classification. The algorithm can achieve a recognition accuracy of 92% in a signal-to-noise ratio environment of \(-\)5-5 dB. Figure 10: Graphical construction of QPSK and 4 ASK signals. 2. The signal sequence recognition method based on CNN. In addition to the CNN-based signal two-dimensional image recognition method, some scholars use CNN to directly extract the features in the signal sequence and complete the classification. Therefore, this section will introduce the CNN-based signal sequence recognition method. (1) IQ sequence  
[PERSON] et al. proposed a DL-based AMR algorithm to identify signals in Orthogonal Frequency Division Multiplexing (OFDM) systems [49]. The authors used convolutional neural networks to train IQ samples of OFDM signals. It can be seen through experiments that when the signal-to-noise ratio is 10 dB, the correct classification probability is higher than 90%, but when the signal-to-noise ratio is lower than 10 dB, the recognition accuracy drops rapidly. In order to enable CNN to work with a small amount of data, the authors of [50] proposed a data-augmented modulation recognition method. The authors first calculate the amplitude, phase, and frequency according to the input IQ signal, and take them as the most basic signal features. Second, the phase sequence of the signals is rearranged according to the distribution of the modulated signals in the constellation diagram, so as to obtain new features. Then, the higher-order spectral information of the signal is obtained to provide new identification clues. Finally, the IQ signal, the amplitude frequency phase of the signal, the reordered IQ sequence, the reordered amplitude frequency phase, and the high-order spectrum of the signal are input into the improved CNN for classification and identification. The experimental results show that the algorithm achieves an average recognition rate of above 95%. However, the feature extraction process of this method is  
Figure 11: Amplitude histogram of modulated signal. relatively complicated, which is not conducive to its use in a changeable communication environment. [PERSON] et al. applied the DL-based AMR algorithm to multiple-input multiple-output (MIMO) systems, and in their work [51] they proposed a CNN-based zero-forcing (ZF) equalization AMR method. Among them, ZF equalization can improve the signal-to-noise ratio of the received signal under the channel state information (CSI) and enhance the accuracy of modulation identification. Therefore, the author inputs the received signal and CSI into ZF equalization, performs vectorization, and finally inputs them into CNN for classification. Through experiments, it can be seen that the recognition accuracy of the ZF equalization AMR algorithm based on CNN reaches more than 90% when the signal-to-noise ratio is--5 dB, which is better than the traditional algorithm based on ANN and higher-order cumulants. [PERSON] et al. [52] proposed a three-dimensional MIMO-OFDM Convolutional Neural Network (MONet) capable of accomplishing efficient AMR in a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system.