# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 3 The Modulation Recognition Method Based on Deep Learning
### Applications of Other Neural Networks in Modulation Recognition
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Input Signal** & **Model** & **Modulation Signal Type** & **Recognition Accuracy** \\ \hline B. [PERSON] et al. [81] & 2018 & Outline constellation map & CNN + GAN & 4 ASK, BPSK, OOPSK, 8 PSK, 16 QAM, 32 QAM, 64 QAM & 100\% (\(-2\) dB) \\ \hline [PERSON]. [PERSON] et al. [82] & 2020 & IQ sequence, cyclic spectrum & CNN + GRU & 2 PSK, 2 ASK, 2 PSK, 4 ASK, 16 QAM, 64 QAM & 100\% (0 dB) \\ \hline [PERSON] et al. [83] & 2021 & IQ sequence & MCLDNN(CNN + LSTM + FC) & WBFM, AM-DSB, AM-SSB, BPSK,  CFPKS, GFSK, +PAM, 16-QAM, 64-QAM, QPSK, and 8 PSK & 90\% (0 dB) \\ \hline [PERSON] et al. [84] & 2021 & IQ sequence & CNN + Bi LSTM + Attention & 2 ASK, 4 ASK, BPSK, OPSK, & 93.14\% (10 dB) \\ \hline [PERSON], [PERSON] et al. [85] & 2021 & Original signal & MCBL (CNN + Bi LSTM + Attention) & 8 PSK, AM-DSB, BPSK, CPFSK, GFSK,  PAM4, QAM6, QPSK,  WBFM & 93\% (0 dB) \\ \hline [PERSON] et al. [86] & 2021 & Time-frequency diagram & ResNeXt WSL(ResNeXt + Attention) & 8 PSK, BPSK, AM-DSB,  QPSK, QAM16, QAM64, CPFSK,  GFSK,  PAM4, and & 90\% (0 dB) \\ \hline [PERSON] et al. [87] & 2022 & Sampled signal & \begin{tabular}{c} \end{tabular} & \begin{tabular}{c} \end{tabular} & \begin{tabular}{c} AM-DSB, AM-SSB, WBFM. \\ + SAFN) \\ \end{tabular} & 8 PSK, BPSK, CPFSK, GFSK, & 84\% (0 dB) \\ \hline [PERSON]. [PERSON] et al. [88] & 2022 & Sampled signal & GRU + CNN & BPSK, QPSK, BPSK, QPSK, 16 QAM, 64 QAM, OFDM & 99.45\% (true channel) \\ \hline [PERSON] et al. [89] & 2022 & IQ sequence & ASCLDNN (Attention + CLDNN) & \begin{tabular}{c} BPSK, 8 PSK, CPFSK, GFSK, PAM4, \\ PAM16, QAM64, QPSK, AM-DSB, \\ AM-SSB, WBFM \\ \end{tabular} & 90\% (0 dB) \\ \hline [PERSON] et al. [90] & 2022 & \
\begin{tabular}{c} IQ sequences and \\ GAF images \\ \end{tabular} &
\begin{tabular}{c} DMFF-CNN (Complex \\ Value Network + ResNet50) \\ \end{tabular} & 2 PSK, AM, DSB, FM, OFDM, QAM16,  QPSK, SSB & 91\% (\(-10\) dB) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of modulation recognition algorithms based on combinatorial neural networks. with principal component analysis. DNN, PNN, and BP networks are better at processing statistical features such as instantaneous features and high-order cumulants of signals. The combination of DBN and a traditional classifier can achieve a better recognition rate. After the introduction of this chapter, we have learned that convolutional neural networks, recurrent neural networks, and various combined neural networks can achieve satisfactory recognition results after training with a large amount of labeled data. However, in some fields, such as spectrum management and national defense information security, a large number of labeled signal data cannot be obtained for neural network training, resulting in the degradation of recognition performance. Therefore, improving the recognition accuracy in a small sample environment is a great challenge. With the continuous development of deep learning technology, some scholars have begun to solve the above problems. In the following section, this paper will introduce the existing modulation recognition algorithm based on deep learning in a small sample environment.