# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 9 Concluding Remarks  
In the previous sections, we reviewed the network models of radio AMR based on DL. The related literature about AMR based on DL was summarized. We found that most of the literature only talked about the advantages of the proposed network model, but few studies mentioned the disadvantages of the model. Below, we summarize the advantages and disadvantages of several models mentioned above. As shown in Table 15, the CNN model has the advantage of local perception. It is good at sensing signal features locally and synthesizing local information at a higher level to obtain global information. CNN also has the advantages of weight sharing and shift-invariance. These can greatly reduce the parameters of model training. However, CNN is a feed-forward neural network with unidirectional propagation, which can only process the current signals. The input signal must be of fixed length. However, in reality, in many cases, the state of the signal at that time is related to the influence of the time before and after. The length of the signal is not exactly the same. The structural form of RNN solved this problem. The input of RNN includes not only the current time, but also the feedback after the output of the current time. As time changes, RNN can also process variable signal sequences. However, it has the problem of disappearing gradients. It cannot solve the problem of long-term dependence. Subsequently, the structural form of LSTM which is the revision of RNN solved the above problems to some extent. LSTM has a memory function for a certain period of time by adding a control unit. LSTM is different from the previous two networks. It is trained in the way of back propagation. However, these functions in turn make LSTM more complex and time-consuming to train. Compared with CNN, RNN and LSTM, DBN is a probability generation model. DBN starts from RBM, based on Bayesian thought and finds out the joint probability distribution of data, so as to automatically obtain the high-level information hidden in the data and difficult to interpret. It can be understood as an unsupervised data coding and the output information has a certain characterization effect on the data. In unsupervised learning, an unlabeled input dataset is provided for the algorithm. The goal is to identify patterns and cluster the data into multiple groups for learning based on similarity [111]. However, its recognition accuracy is not very highand the process of model training is also very complicated. Therefore, when we build the model, we might as well consider designing some hybrid models. By comprehensively utilizing the advantages of each model, more effects can be obtained. For example, in the CLDNN model mentioned in [57], the convolution layer is followed by the cyclic layer. In this case, CNN and LSTM achieve complementary effects, because CNN is good at reducing frequency variation and LSTM is good at temporal modeling [112; 113]. As a key part of communication signal processing, radio signal modulation recognition has become a research hotspot with the development of artificial intelligence, including DL, neural networks and others. This paper provides a literature survey and summarizes the development and state-of-the-art of radio modulation recognition based on DL. This paper also summarizes the existing methods of radio modulation recognition based on neural networks and open radio signal datasets. In addition, a CNN network for radio modulation recognition is designed and the effectiveness of the proposed network is verified. A comparison of the proposed network with the previously existing ones has been carried out to show its advantages. Finally, the common evaluation parameters of the existing classical methods are introduced. The problems to be solved in the field of radio modulation recognition and the future research direction are discussed.