# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 7 Summary  
As a key technology of the physical layer, automatic modulation recognition has important research value and application prospects. With the complexity of communication scenarios, AMR technology will face more severe challenges, so researchers need to continuously develop new AMR methods to solve new problems. This paper expounds the research situation of advanced AMR algorithms based on deep learning in the past five years and organizes some of the references mentioned in this paper into Table 4. Column 1 in Table 4 is some of the references mentioned in this paper, and column 7 in Table 4 is the state-of-the-art technology mentioned in the references in column 1. We have sorted out these contents and formed a comparison table, so that readers can intuitively understand the technical starting points of the references mentioned in this article and hope to provide some references for future research.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Reference** & **A** & **Jather** & **Year** & **Input Signal Type** & **Model** & **Recognition** & **Advanced** & **Techniques** & **Number** & **Input Signal** & **Model** & **Recognition** \\
**A** & & & & & **Accuracy** & **Measitized in** & **Reference A** & & **Type** & **Type** & **Type** & **Accuracy** \\ \hline
[4] & \begin{tabular}{c} Pengzhongs \\ \(\mu\)-d. \\ \end{tabular} & 2019 & Constellation Chart & \begin{tabular}{c} GongNet, AlexNet \\ \end{tabular} & 90\%(\cdot\)(3 dB) & [118] & Fun Wang & 2019 & \
\begin{tabular}{c} Constellation \\ Chart \\ \end{tabular} &
\begin{tabular}{c} GCN-DNN \\ \end{tabular} & 95\%(\cdot\)(3 dB) \\ \hline
[4] & \begin{tabular}{c} ZhouNet \(\mu\)-d. \\ \end{tabular} & 2019 & Eye Chart & \begin{tabular}{c} Improved ResNet \\ \end{tabular} & 90\%(\cdot\)(5 dB) & [10] & Penphemplings & 2419 & \
\begin{tabular}{c} Constellation \\ Chart \\ \end{tabular} &
\begin{tabular}{c} Cong-In-Net, AlexNet \\ \end{tabular} & 90\%(\cdot\)(3 dB) \\ \hline
[4] & \begin{tabular}{c} Xing Zhou \\ \(\mu\)-d. \\ \end{tabular} & 2019 & \
\begin{tabular}{c} Constellation and \\ curve of depth \\ \end{tabular} & 92\%(\cdot\)(3 dB) & [10] & Penphemplings & 2219 &
\begin{tabular}{c} Constellation \\ Chart \\ \end{tabular} & Cong-In-Net, AlexNet & 90\%(\cdot\)(4 dB) \\ \hline
[4] & \begin{tabular}{c} YongWang \\ \(\mu\)-d. \\ \end{tabular} & 2021 & \
\begin{tabular}{c} Time and frequency \\ diagram \\ \end{tabular} & Sep-ResNet & 90.44\% (\cdot\)(10 dB) & [17] & 21\%yu Qa & 2020 &
\begin{tabular}{c} Time and \\ frequency \\ diagram \\ \end{tabular} & CNN & 96.1\%(\cdot\)(\(\cdot\)6 dB) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Technical comparison table of some references.  
**Author Contributions:** Conceptualization, Z.L.; methodology; investigation, W.X. and Q.H.; writing--original draft preparation, W.X. and Q.H.; writing--review and editing, Z.L.; supervision, Z.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in by the National Natural Science Foundation of China under Grant 61801319, the Sichuan Science and Technology Program under Grant 2020 JDJQ0061, 2021 YFG0099, 2020 JDJQ0075, 2019 YJ0476, and 2020 YFSY0027, the Innovation Fund of Chinese Universities under Grant 2020 HYA04001, the Wuliangye project under Grant CXY2020 ZR006, the Sichuan University of Science and Engineering Talent Introduction Project under Grant 2020 RC33. This project is supported by the Postgraduate Innovation Fund Project of Sichuan University of Science and Engineering.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.