# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 3 Radio Modulation Recognition Methods Based on Deep Learning
### Recent Research Trends of AMC Based on DL  
By reading through the literature, we found that most of the previous studies had focused on the design of network models. Most network models were based on the extension or integration of basic models such as CNN, RNN, LSTM and DBN. In order to further improve the performance of DL-based AMC, there are some new research trends. These can be roughly divided into the following three categories:  
The first category considers the transform of input signals. The research of some articles not only focuses on the design of the network model but also on the signal form of the input network. The original IQ signal is not used as the input of the network, but the IQ signal is transformed differently. The transformed form is used as the input of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Author**} & \multicolumn{2}{c}{**The Innovation of the**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Evaluation Parameter**} & \multirow{2}{*}{**The Technology of DL**} \\  & & **Paper** & & & & \\ \hline \multirow{2}{*}{2019} & \multirow{2}{*}{[PERSON] et al. [66]} & Deep hierarchical & \multirow{2}{*}{RadioML2016.10a} & The accuracy can reach & SNR as a weight in \\  & & network (DHN) based & & 93\%. & training; \\  & & on CNN. & & & \\ \hline \multirow{2}{*}{2021} & \multirow{2}{*}{[PERSON] et al. [68]} & An AMR method based & \multirow{2}{*}{RadioML2016.10a} & The recognition & \multirow{2}{*}{DenseNet;} \\  & & on DenseNet + BLSTM & & & & \\  & & + DNN network. & & & & \\  & & & & & \\ \hline \multirow{2}{*}{2021} & \multirow{2}{*}{[PERSON] et al. [69]} & An AMR method based & \multirow{2}{*}{RadioML2016.04c;} & The comprehensive & \multirow{2}{*}{CNN;} \\  & & on a CNN–GRU hybrid & & & & \\  & network. & & & 60.64\% and 73.22\%, & \\  & & & & &  respectively. & \\ \hline \multirow{2}{*}{2021} & \multirow{2}{*}{[PERSON] et al. [71]} & An AMC method based & \multirow{2}{*}{RadioML2016.10a;} & The recognition & \multirow{2}{*}{CNN;} \\  & & on CNN + GRU + DNN & & & 93.5\% and 90.38\% on & \\  & & network. & & & & \\ \hline \multirow{2}{*}{2021} & \multirow{2}{*}{[PERSON] et al. [72]} & An AMR method of & \multirow{2}{*}{RadioML2016.10a;} & The recognition & \multirow{2}{*}{CNN;} \\  & & hierarchical & & & \\  & multifeature fusion & & & accuracy is higher than & LSTM \\  & & based on multidimensional CNN & & & \\  & & and LSTM. & & & \\ \hline \multirow{2}{*}{2021} & \multirow{2}{*}{[PERSON] et al. [74]} & A novel multi-cue & \multirow{2}{*}{RadioML2016.10a;} & The recognition & \multirow{2}{*}{CNN;} \\  & & fusion network for & & & 97.8\% and 96.1\% on & IndRNN; \\  & & AMR. & & & \\  & & AMR. & & & \\  & & & & & \\ \hline \multirow{2}{*}{2021} & \multirow{2}{*}{[PERSON] et al. [70]} & The GRU based on & \multirow{2}{*}{Created dataset 1} & The recognition rate is & \multirow{2}{*}{CNN;} \\  & & feature extraction and & & & 100\% when the SNR is & GRU \\ \cline{1-1}  & & CNN based on cyclic & & \(-1\) dB. & \\ \cline{1-1}  & & spectrum are & & & \\ \cline{1-1}  & & combined. & & & \\ \hline \multirow{2}{*}{2022} & \multirow{2}{*}{[PERSON] et al. [73]} & An AMR method based & \multirow{2}{*}{RadioML2016.04c} & The recognition & \multirow{2}{*}{CNN;} \\  & & on a novel multi-path & features fusion & & 18 dB SNR. & LSTM \\ \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 5: _Cont._the network so that the network can better extract the characteristics of the signal. This improves the performance of AMC. In 2022, [PERSON] et al. [25] orthogonally projected the original IQ signal to obtain the constellation diagrams of the signal. An AMC algorithm based on CNNs was proposed. The RT of constellation diagrams with different modulation types is used as input of the network.