# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 3 Radio Modulation Recognition Methods Based on Deep Learning
### Other Models Based on DL Networks  
In addition to the above several common DL network models, there are some not so commonly used DL network models for radio signal modulation recognition. They are as follows: AE, CLDNN, dynamic multi-pooling convolutional neural network (DMCNN), Gated Recurrent Unit (GRU), Adversarial Training for Supervised and Semi-Supervised Learning and so on. The relevant literature is summarized in Table 5 and described in the following. [PERSON] et al. [68] proposed a hybrid network model. Its network structure consists of DenseNet, BLSTM and DNN. They tested and proved the effectiveness of the proposed algorithm by using the RadioML2016.10a dataset. Experimental results showed that the classification accuracy of this network model was much higher than that of the benchmark model at high \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Author** & \begin{tabular}{c} **The Innovation of** \\ **the Paper** \\ \end{tabular} & **Dataset** & \
\begin{tabular}{c} **Evaluation** \\ **Parameter** \\ \end{tabular} &
\begin{tabular}{c} **The Technology of** \\ **DL** \\ \end{tabular} \\ \hline
2015 & [PERSON] et al. [102] & \
\begin{tabular}{c} User centered DBN \\ model \\ \end{tabular} & Actual sampling data &
\begin{tabular}{c} Short time; \\ Accuracy increased. \\ \end{tabular} & DBN model \\ \hline
2016 & [PERSON] et al. [61] & \
\begin{tabular}{c} Collaborative \\ Bayesian compressed \\ spectrum detection \\ method based on \\ RBM. \\ \end{tabular} & Created dataset 1 &
\begin{tabular}{c} Improve detection \\ accuracy; \\ Enhance \\ anti-interference \\ capability \\ \end{tabular} & RBM \\ \hline
2016 & [PERSON] et al. [60] & DBN with anti noise & Created dataset 1 & \
\begin{tabular}{c} The accuracy can \\ reach more than 90\%. \\ \end{tabular} &
\begin{tabular}{c} Feature \\ representation \\ mechanism based on \\ SCF; \\ DBN Network. \\ \end{tabular} \\ \hline
2017 & [PERSON] et al. [21] & \begin{tabular}{c} DBN based on low \\ complexity. \\ \end{tabular} & \begin{tabular}{c} Simulation creation \\ dataset \\ \end{tabular} & \
\begin{tabular}{c} the classification \\ accuracy can reach \\ more than 90\%. \\ \end{tabular} &
\begin{tabular}{c} Feature \\ mechanism based on \\ SCF; \\ DBN Network \\ \end{tabular} \\ \hline
2018 & [PERSON] et al. [59] & DBN & \begin{tabular}{c} Simulation creation \\ dataset \\ \end{tabular} & \
\begin{tabular}{c} The average \\ recognition rate is \\ 92.12\%. \\ \end{tabular} &
\begin{tabular}{c} Unsupervised greedy \\ algorithm; \\ DBN Network. \\ \end{tabular} \\ \hline \hline \end{tabular}  
* The dataset created refers to the data signals designed by the authors according to their own needs by using simulation software.  
\end{table} 
Table 4: Radio modulation recognition networks based on DBN. SNR. It could extract deeper information. [PERSON] et al. [65] proposed a signal classifier production antagonism network, increased the encoder network and signal space transformation module and achieved significant accuracy improvement. [PERSON] et al. [66] proposed a new deep hierarchical network (DHN) based on CNN, which combined shallow features with advanced features and used SNR as the weight in training. [PERSON] et al. [67] combined CNN and the short-term memory architecture into a deep neural network CLDNN, which improved the accuracy by about 13.5% compared with the original CNN model. [PERSON] et al. [22] proposed an unsorted input data classifier (UDNN) based on k-sparse self-encoder, which used the power of DNN to learn advanced abstraction from raw input data to omit Klog (K) comparison operations as much as possible. [PERSON] et al. [69] proposed an AMR method based on a CNN-GRU hybrid network. This method used different structures to extract and classify features with different dimensions automatically. The comprehensive recognition accuracy on RadioML2016.04c and RadioML2016.10a was 60.64% and 73.2%, respectively. [PERSON] et al.