# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 3 The Modulation Recognition Method Based on Deep Learning
### Application of the Recurrent Neural Network in Modulation Recognition
#### 3.2.2 Modulation Recognition Based on Recurrent Neural Network  
The automatic modulation recognition work is very dependent on the characteristics of the signal. For different types of features, the extraction and classification methods are also different. It is mentioned in the previous content that with CNN it is easier to extract the features in the two-dimensional image of the signal, but for other forms of features, the extraction and classification effect of CNN is not so good. For example, pulse repetition interval (PRI) features in radar signals are not conducive to extraction and classification using CNN, because the PRI of the signal will greatly hinder the online application of CNN [60]. Subsequently, some scholars proved that the LSTM model can extract PRI features better than the CNN model [61]. In 2020, the authors of [62], taking into account the sequence characteristics of PRI, proposed a model combining attention mechanism with GRU to identify the modulation type of radar signal. The model can improve the recognition rate in the presence of a high ratio of missing and spurious pulses. This article proposes to use a one-hot vector to represent the PRI sequence and then reduce the dimensionality of the sequence and input it into the attention-based GRU model, which can save the sequential patterns contained in the PRI sequence and finally output it through the GRU. This article proposes to use a one-hot vector to represent the PRI sequence and then reduce the dimensionality of the sequence and input it into the attention-based GRU model, which can save the sequential patterns contained in the PRI sequence and finally output it through the GRU. Inspired by the fact that the nodes of the potential layer in the LSTM model can retain the dynamic time-domain characteristics of the information, some scholars proposed a low SNR modulation recognition method based on LSTM [63]. This work uses an LSTM network to construct a signal-to-noise ratio classifier, a denoising auto-encoder, and finally a recognition classifier. The signal-to-noise ratio classifier consists of three LSTM layers and a fully connected layer, which can divide the signal into low signal-to-noise ratio signals and high signal-to-noise ratio signals according to the set threshold. The denoising auto-encoder is composed of five bidirectional LSTM layers, which can denoise signals with a low signal-to-noise ratio. Finally, a modulation recognition structure based on LSTM is designed. The experimental results show that the modulation recognition model based on LSTM can achieve an average recognition rate of more than 90% at a signal-to-noise ratio of 0-8 dB, but the recognition rate is still low with a signal-to-noise ratio of \(-\)10 dB\(\rightarrow\)\(-\)2 dB. [PERSON] et al. used a model combining a self-attention mechanism and bidirectional LSTM to identify the modulation mode of a radar signal [64]. The method can accurately identify eight types of radar modulation signals under low signal-to-noise ratio and the recognition rate is as high as 95% at \(-\)10 dB. Experiments show that the recognition accuracy  
Figure 12: Structure diagram of cyclic neural network. of the model is better than CLDN, DRCNN, Seq-CNN, and Seq-Res networks. [PERSON] et al. (2018) designed an end-to-end modulation identification method based on LSTM and GRU, which can directly obtain the modulation type from the sampled signal. The experimental results show that the end-to-end modulation recognition method proposed by the author can achieve a recognition rate of 90% for each type of modulated signal. In order to solve the local dependency constraints of CNN and RNN in extracting signal features, [PERSON] et al. proposed a transformer based connected sequential neural network structure (ctdnn) ([PERSON] et al., 2019).