# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 3 The Modulation Recognition Method Based on Deep Learning
### Application of Combination Neural Network in Modulation Recognition  
According to the previous description, CNN has a strong ability to process image information, and RNN has excellent performance when processing IQ signals directly. Therefore, some scholars combined CNN and RNN to form the CLDNN model ([PERSON] et al., 2019), and CLDNN gave full play to the advantages of these two models and further improved the accuracy of modulation recognition. Since then, more and more scholars have devoted themselves to studying the application of combined neural networks in modulation recognition. In this section, we summarize the modulation recognition method based on a combined network. [PERSON] proposed an MCF network composed of the Information Cue Multi-Stream Module (SCMS) and the Visual Cue Recognition Module (VCD) ([PERSON] et al., 2019). The network realizes the parallel feature extraction of the IQ, A\(\Phi\), and signal constellation of the signal. The SCMS module extracts the features of the IQ signal and the A\(\Phi\) signal and uses the VCD to extract the features of the signal from the constellation. Finally, the extracted features \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Input Signal** & **Model** & **Modulation Signal Type** & \begin{tabular}{c} **Recognition** \\ **Accuracy** \\ \end{tabular} \\ \hline D. [PERSON] et al. (2018) & 2017 & IQ signal & Two-layer GRU & \begin{tabular}{c} WB-FM, AM-SSB, AM- \\ DSB, BPSK, QPSK, 8 PSK, 16 QAM, 64 QAM, \\ BFSK, CPFSK, PAM4 \\ \end{tabular} & 91\% (-4\) dB \\ \hline [PERSON], [PERSON] et al. (2018) & 2019 & \begin{tabular}{c} Amplitude phase of \\ IQ signal \\ \end{tabular} & LSTM & \begin{tabular}{c} 8 PSK, AM-DSB-BPSK, PFSK, GFSK, PAM4, \\ QAM16, QAM64, QPSK, WBFM \\ \end{tabular} & 80\% (6\) dB \\ \hline [PERSON], [PERSON] et al. (2019) & 2020 & IQ signal & HSNet & \begin{tabular}{c} 16 QAM, 32 QAM, BPSK, QPSK, BPSK, PAM, \\ CPFSK, GFSK, FM, SSB \\ \end{tabular} & 86\% (0\) dB \\ \hline [PERSON]. [PERSON] et al. (2019) & 2021 & \begin{tabular}{c} Polar coordinate \\ representation of \\ the signal \\ \end{tabular} & IRS + LSTM & \begin{tabular}{c} BPSK, QPSK, 8 PSK, 16 QAM, 64 QAM, GFSK, \\ CPFSK, 4 QAM, WBFM, AM–DSB \\ \end{tabular} & 90\% (0\) dB \\ \hline [PERSON] et al. (2018) & 2021 & IQ signal & DCN-BiLSTM & \begin{tabular}{c} AM-DSB, AW-SSB, WBFM, BPSK, SPSK, \\ QPSK, CPFSK, GFSK, PAM4, \\ 16 QAM, 64 QAM \\ \end{tabular} & 90\% (4\) dB \\ \hline [PERSON] et al. (2018) & 2022 & IQ signal & LSTM & \begin{tabular}{c} 8 PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, \\ PAM4, QAM16, QAM64, QPSK, WBFM \\ \end{tabular} & 90\% (8\) dB \\ \hline [PERSON] (2018) & 2022 & Original signal & \begin{tabular}{c} EDL-MSC (GRU + \\ Bi LSTM + SSAE) \\ \end{tabular} & 8 PSK, BPSK, 16 QAM, 64 QAM, QPSK & 92\% (-4\) dB \\ \hline UdayaDampage ([PERSON] et al., 2019) & 2022 & IQ signal & LSTM + Bi LSTM & \
\begin{tabular}{c} BPSK, QPSK, 16 QAM, \\ 64 QAM, 256 QAM \\ \end{tabular} & 90\% (0\) dB \\ \hline [PERSON] (2018) & 2022 & IQ signal & S-QRNN &
\begin{tabular}{c} OOK, 4 ASK, 8 ASK, BPSK, QPSK, \\ 16 PSK, 32 PSK, 16 APSK, 32 APSK, 64 APSK, \\ 128 APSK, 16 QAM et \\ \end{tabular} & 90\% (5\) dB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of modulation recognition algorithms based on cyclical neural networks. are fused and classified. The author compared the MCF network with CM+CNN, SCNN, LSTM, and CLDNN network models during the experiment. The results show that the MCF network has better recognition accuracy. When the signal-to-noise ratio is greater than 0 dB, the recognition accuracy is close to 100%. The authors of [78] proposed a new deep learning-based attention collaboration framework, which includes CNN, RNN, and GAN networks. Among them, ACGAN is used to expand the original data, and then CNN and RNN are used to extract the spatial and temporal characteristics of the signal, respectively.