# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 3 Radio Modulation Recognition Methods Based on Deep Learning
### Dbn  
DBN is another typical DL algorithm proposed by [PERSON] in 2006 [PERSON] (2006). It is a probability generation model and widely used in natural language processing [PERSON] (2006); [PERSON] (2007); [PERSON] (2008) and image recognition [PERSON] (2008); [PERSON] (2009); [PERSON] (2010); [PERSON] (2011).  
Figure 5 shows the classic DBN network structure, which is composed of several restricted Boltzmann machine (RBM) layers. The network is \"limited\" to a visible layer (i.e., input layer) and a hidden layer. Each RBM has two layers: an upper hidden layer and a lower visible layer. The DBN training process is divided into two steps. The first step is pretreatment and uses layerwise training. The lower layer serves as the input to the upper layer. The second step is fine-tuning. It is supervised to train the last layer and the error generated by result comparison is propagated backward layer by layer, fine-tuning the overall weight.  
Figure 5: DBN network structure [PERSON] (2006).  
\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** & **Evaluation Parameter** &
\begin{tabular}{c} **The Technology of** \\ **DL** \\ \end{tabular} \\ \hline
2017 & [PERSON] et al. [PERSON] et al. (2017) & RNN & RadioML2016.10a &
\begin{tabular}{c} The classification \\ accuracy can reach \\ 91\%. \\ \end{tabular} & RNN \\ \hline
2017 & [PERSON] et al. [PERSON] et al. (2017) & LSTM & RadioML2016.10a &
\begin{tabular}{c} The classification \\ accuracy is about 90\%. \\ \end{tabular} & LSTM \\ \hline
2018 & [PERSON] et al. [PERSON] et al. (2018) Two layer LSTM. & RadioML2016.10a &
\begin{tabular}{c} The average \\ classification accuracy is close to 90\%. \\ \end{tabular} & RNN; LSTM. \\ \hline
2018 & [PERSON] et al. [PERSON] et al. (2018) & \
\begin{tabular}{c} LSTM; \\ Preprocess signal \\ \end{tabular} & RadioML2016.10a &
\begin{tabular}{c} The accuracy is \\ improved by 8\%. \\ \end{tabular} & LSTM \\ \hline
2018 & [PERSON] et al. [PERSON] et al. (2018) & Improved LSTM & RadioML2016.10a &
\begin{tabular}{c} The accuracy can \\ reach 93\%. \\ \end{tabular} & LSTM \\ \hline
2019 & [PERSON] et al. [PERSON] et al. (2019) & LSTM & Created dataset 1 &
\begin{tabular}{c} The accuracy can \\ reach 94.72\% \\ \end{tabular} & 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 3: Radio modulation recognition networks based on RNN and LSTM.  
There is not much literature on radio modulation recognition methods based on DBN. At present, there are several as summarized in Table 4 and described below. [PERSON] et al. [59] used deep confidence network and unsupervised greedy algorithm to pre-train RBM. The result obtained was used as the initial value of the supervised learning training probability model and the time IQ data representation was used to identify the modulation types, which improved the recognition rate. In the cognitive radio signal modulation pattern recognition algorithm proposed by [PERSON] et al. [60] and [PERSON] et al. [21] for DBN, the spectral correlation function was used as the characteristic representation of the received signal even in the presence of environmental noise. [PERSON] et al. [102] proposed a DBN-based DL algorithm which was applied in the main user classification, and significantly reduced the number of tagged data. At the same time it has a better recognition rate than CR engine using traditional strategies such as shallow learning, with a detection accuracy of more than 90% and a classification accuracy of more than 85%. [PERSON] et al. [61] proposed a cooperative Bayesian compression spectrum detection method based on RBM, which used Bayesian compression sensing model to detect wideband sparse signals and then used RBM learning to implement fusion decision based on multiuser recovery signals.