# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 3 Radio Modulation Recognition Methods Based on Deep Learning
### Cnn
\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
2019 & [PERSON] et al. [34] & Combination of two & \begin{tabular}{c} Constellation dataset \\ CNN based on DL; \\ CNN based on \\ Constellation. \\ \end{tabular} & \begin{tabular}{c} Constellation dataset \\ created. \\ \end{tabular} & \
\begin{tabular}{c} The accuracy of the \\ former can reach \\ more than 95\%; \\ The latter precision is \\ close to 100\%. \\ \end{tabular} &
\begin{tabular}{c} Planisphere; \\ DrCNN. \\ \end{tabular} \\ \hline
2019 & [PERSON] et al. [37] & \begin{tabular}{c} AMC with multi \\ feature fusion based \\ on CNN. \\ \end{tabular} & \begin{tabular}{c} RadioML2016.10a \\ \end{tabular} & \
\begin{tabular}{c} The average accuracy \\ is 80\%; \\ Reduced training \\ time. \\ \end{tabular} &
\begin{tabular}{c} Multi feature fusion; \\ CNN architecture. \\ \end{tabular} \\ \hline
2019 & [PERSON] et al. [38] & \begin{tabular}{c} Geneamr based on \\ two CNN. \\ \end{tabular} & Created dataset 1 & \
\begin{tabular}{c} The accuracy can \\ reach 98\%. \\ \end{tabular} &
\begin{tabular}{c} CNN architecture. \\ \end{tabular} \\ \hline
2019 & [PERSON] et al [39] & \begin{tabular}{c} AMR of CNN based \\ on three fusion \\ methods. \\ \end{tabular} & Created dataset 1 & \
\begin{tabular}{c} The accuracy is 96\%, \\ 97\%, 98\%. \\ \end{tabular} &
\begin{tabular}{c} CNN architecture. \\ \end{tabular} \\ \hline
2020 & [PERSON] et al. [41] & \begin{tabular}{c} Dense layer dropout \\ CNN (DDrCNN). \\ \end{tabular} & Created dataset 1 & \
\begin{tabular}{c} More than 97\% \\ accuracy can be \\ achieved. \\ \end{tabular} &
\begin{tabular}{c} CNN architecture; \\ Classification cross \\ entropy. \\ \end{tabular} \\ \hline
2021 & [PERSON] et al. [51] & \begin{tabular}{c} An AMC method \\ based on lightweight \\ CNN. \\ \end{tabular} & \begin{tabular}{c} RadioML2016.10a; \\ RadioML2018.01a \\ \end{tabular} & \
\begin{tabular}{c} The proposed \\ network can save \\ 70\(\sim\)98\% model \\ parameters and 30\(\sim\)99\% inference \\ time. \\ \end{tabular} &
\begin{tabular}{c} CNN; \\ Fundamental architecture. \\ \end{tabular} \\ \hline
2021 & [PERSON] et al. [52] & \begin{tabular}{c} An AMC method \\ based on \\ multiple-scale CNN. \\ \end{tabular} & Created dataset 1 & \
\begin{tabular}{c} The averaged \\ classification accuracy \\ reaches \\ approximately 97.7\% \\ at 4 dB SNR. \\ \end{tabular} &
\begin{tabular}{c} CNN; \\ Fundamental architecture. \\ \end{tabular} \\ \hline
2022 & [PERSON] et al. [25] & \begin{tabular}{c} An AMC method \\ based on CNN, which \\ uses random transform \\ of constellation \\ diagrams as input. \\ \end{tabular} & Created dataset 1 & \
\begin{tabular}{c} The classification \\ accuracy reaches \\ 100\% at 5 dB SNR. \\ \end{tabular} &
\begin{tabular}{c} CNN; AlexNet; VGG. \\ \end{tabular} \\ \hline
2022 & [PERSON] et al. [53] & \
\begin{tabular}{c} A dilated CNN for \\ AMR. \\ \end{tabular} & Created dataset 1 &
\begin{tabular}{c} The recognition \\ accuracy under low \\ SNR is significantly \\ improved. \\ \end{tabular} & Dilated CNN. \\ \hline
2022 & [PERSON] et al. [54] & \begin{tabular}{c} An AMR method \\ based on a multi-scale \\ convolution deep \\ network. \\ \end{tabular} & \
\begin{tabular}{c} The recognition \\ accuracy can reach \\ 98.7\%. \\ \end{tabular} &
\begin{tabular}{c} CNN; \\ Attention \\ mechanisms. \\ \end{tabular} \\ \hline
2022 & [PERSON] et al. [55] & \begin{tabular}{c} An AMR framework \\ based on CNN with \\ time-frequency \\ attention mechanism. \\ \end{tabular} & \begin{tabular}{c} RadioML2018.01a; \\ RatioML2016.10b \\ \end{tabular} & \
\begin{tabular}{c} This method has \\ higher recognition \\ rate and fewer \\ parameters. \\ \end{tabular} &
\begin{tabular}{c} CNN; \\ Attention \\ mechanisms. \\ \end{tabular} \\ \hline
2022 & [PERSON] et al. [56] & \begin{tabular}{c} Five CNN models are \\ proposed for AMC. \\ \end{tabular} & HisarMod2019.1 & \
\begin{tabular}{c} The highest accuracy \\ can reach 97.5\%. \\ \end{tabular} &
\begin{tabular}{c} ResNet18; \\ SqueezeNet; \\ GoogleNet; \\ MobileNet; RepVGG. \\ \end{tabular} \\ \hline \hline \end{tabular}  
\end{table}