# 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
[71] proposed an AMC method based on a hybrid neural network composed of a shallow CNN, a GRU and a DNN. The recognition accuracy reached 93.5% and 90.38% on RadioML2016.10a and RadioML2016.10b, respectively. [PERSON] et al. [72] proposed an AMC method of hierarchical multifeature fusion based on multidimensional CNN and LSTM. Multidimensional CNN compensated the interactive features extracted by two-dimensional convolution filter with the features extracted by one-dimensional filter. The LSTM layer was used to extract the time characteristics of the signal. The recognition accuracy was higher than other methods. [PERSON] et al. [74] proposed a novel multi-cue fusion network for AMR. The network consisted of a signal cue multi-stream (SCMS) module and a visual cue discrimination (VCD) module. The SCM module based on CNN and Independently Recurrent Neural Network (IndRNN) was used to extract two signal cues (In-phase/Quadrature and amplitude-phase), which aimed to explore various differences and make use of the supplement of multiple data forms. The VCD module took the constellation map as the visual clue and used CNN to extract the structural information of the map. The recognition accuracy reached 97.8% and 96.1% on RadioML2016.10a and RadioML2018.01a, respectively. [PERSON] et al. [70] proposed a modulation recognition method, which combined GRU based on feature extraction with CNN based on cyclic spectrum. The results showed that this method greatly improved the modulation recognition rate under low SNR. The recognition rate was more than 90% when the SNR was \(-6\) dB. The recognition rate was 100% when the SNR was \(-1\) dB. Aiming at the problem of low accuracy of wireless signal modulation recognition, [PERSON] et al. [73] proposed a rough and fine feature fusion network. The method of combining rough and fine feature fusion module with LSTM achieved better recognition accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Author**} & **The Innovation of the** & **Dataset** & **Evaluation Parameter** & **The Technology of DL** \\ \cline{3-6}  & & **Paper** & & & \\ \hline \multirow{3}{*}{2017} & [PERSON] et al. [67] & Convolution long short & & & CNN; \\  & & term deep neural & & 88.5\%. & Convolution long short \\  & & network (CLDNN) & & & & term network. \\ \hline \multirow{3}{*}{2017} & [PERSON] et al. [22] & Data classifier (udnn) & Signals actually & The classification & sparse autoencoder; \\  & & & collected. & accuracy can reach 95\%. & Classification cross \\  & & & & & entropy. \\ \hline \multirow{3}{*}{2017} & [PERSON] et al. [64] & Deep automatic encoder & & & When SNR is 10 dB, \\  & & network. & & the recognition rate can & AE \\ \hline \multirow{3}{*}{2018} & \multirow{3}{*}{[PERSON] et al. [65]} & Semi supervised & & & \\  & & learning method for & & & accuracy is 91\%. & STN network structure; \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Other radio modulation recognition networks.