# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 3 The Modulation Recognition Method Based on Deep Learning
### Applications of Other Neural Networks in Modulation Recognition  
In the previous three subsections, we introduced the application of classical deep learning networks and combinatorial neural networks in modulation recognition, but in fact, there is much modulation recognition research based on other deep neural networks, for example, Multilayer Perceptron (MLP), Deep Neural Network (DNN), Probabilistic Neural Network (PNN), Deep Belief Network (DBN), and Back Propagation neural network (BP). Therefore, in this section, this paper will introduce the applications of MLP, DNN, PNN, DBN, and BP networks in AMR. The authors of [91] studied an efficient modulation recognition model, which includes three modules, namely feature extraction, feature optimization, and classifier design. The model uses Principal Component Analysis (PCA) to optimize features in the feature extraction module and uses MLP and Radial Basis Function (RBF) as classifiers, respectively. Experiments show that the recognition accuracy rate of the algorithm based on PCA-MLP is always above 95% in the range of signal-to-noise ratio of \(-\)10-10 dB, which has better stability than the algorithm based on PCA-RBF. The authors of [92], proposed a new method that can quickly complete modulation recognition. The authors use the sixth-order cumulant of the signal as a feature, which is then fed into a DNN to complete the recognition. The entire recognition model can complete online and offline recognition and has high practicability. [PERSON] et al. designed a PNN-based modulation recognition algorithm in [93]. The authors fused the temporal instantaneous characteristics of the signal with the higher-order cumulants. Finally, PNN is used to complete modulation recognition according to the fused features. Experiments show that the algorithm can achieve a recognition accuracy of 99.8% at 0 dB. The deep belief network is a typical unsupervised learning network, which can use the stacked neural network to extract features from the received signal and then use a traditional classifier to determine the modulation type [94]. The authors of [95], proposed a spatial laser pass pulse position modulation and demodulation system based on the cascade of DBN and SVM, which can realize modulation identification and demodulation. The DBN network is used to identify the modulation type, and the SVM is used to classify the demodulation results. Experiments show that the DBN-SVM demodulation system can effectively alleviate the phenomenon of channel fading, and it is close to the maximum likelihood detection method in atmospheric turbulence, indicating that the performance of the algorithm is very good. The authors of [96] combined multi-layer DBN and BP networks to design an AMR classifier, that can detect eight kinds of radar modulated signals (CW, PSK, DPSK, and FSK when the signal-to-noise ratio is greater than 10 dB. MP, LFM, and NLFM with a recognition probability of 100%), reflecting the excellent recognition performance of deep learning. The authors of [97] proposed a BP network model (BP) based on a bird swarm algorithm. First, the bird swarm algorithm was optimized for the BP network, and then the MQAM signal was identified. Experiments show that the recognition accuracy of the algorithm reaches more than 98% and has good classification. [PERSON] et al. used four BP networks to form a modulation recognition classifier [98], which was able to achieve a recognition rate of more than 90% at a signal-to-noise ratio of 11 dB. However, the recognition accuracy of this classifier under low SNR is not known, so further research is needed. In this section, we briefly introduce the applications of MLP, DNN, DNN, DBN, and BP networks in modulation recognition. It can be seen that MLP can be jointly optimized