# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
###### Abstract
When ML algorithms fail in the prediction process, expert engineers are required to adjust them. However, with DL, features are learned automatically at multiple levels. DL models can learn and extract features unsupervised from unlabeled or unstructured data, making decisions without human supervision. Therefore, DL is more automatic and efficient than ML. More recently, DL has also been applied to the field of radio modulation recognition. The process of radio AMR based on DL can be roughly described as below. First, the DL algorithm is used to code and learn the radio time domain signals; then, through deep learning, the similarity of the feature vectors or the same characteristics of similar modulation signals can be matched automatically; finally, the radio characteristics can be fine-tuned from top to bottom using the class label information of training data to obtain a radio in DL representation vector to train the fully connected classifier for radio modulation classification. In this paper, we collected the milestone works and the latest progress of AMC based on DL in the past seven years. The papers in the reference section are downloaded from the following sources: Google Scholar, MPDI, IEEE Explore, Scopus Elsevier, Springer, Web of Science, Research Gate, arXiv, etc. This paper mainly reviews the methods of AMC based on DL. There are many DL-based AMC methods. According to the different DL network models used, the methods are divided into four categories: CNN-based AMR method, RNN-based AMR method, DBN-based AMR method and hybrid network-based AMR method. By observing the literature of the recent years, the latest and novel methods of AMC based on DL are summarized. Considering that datasets are necessary for signal training and testing with DL networks, this paper also summarizes the reference papers that propose different datasets. In recent decades, radio AMR has made significant progress, especially in recent years. DL has brought advancement to many research areas, including radio AMR. The aim of this survey is to comprehensively summarize the relevant work of DL in the field of radio AMR. The main contributions of this paper are summarized as follows:  
* We briefly review the relevant progress of DL-based AMR in the past seven years and point out the benefits of DL technology for AMR research. * We summarize the existing methods of DL-based AMR and classify them according to CNN, RNN, DBN and hybrid network. In addition, the new research methods and research trends in the past year are also given. * We investigate the radio signal datasets used by DL-based AMR. They are introduced in detail. * We propose a CNN-based AMR method, which is proved to have good performance and high recognition accuracy through simulation experiments. * We introduce the commonly used evaluation parameters of DL-based AMR for clearer understanding of the relevant literature. * We discuss and compare the existing AMC methods based on DL in detail. The existing problems and future research directions are summarized. This paper is organized as follows. In Section 2, we introduce the related work of AMC based on DL. Next, Section 3 summarizes the existing methods of AMR based on DL. Section 4 summarizes and describes the published radio signal datasets. In Section 5, a radio modulation recognition method based on CNN is proposed and its experimental simulation and verification are carried out. Section 6 introduces the commonly used evaluation parameters. Section 7 discusses the existing DL-based AMR methods. Section 8 summarizes and discusses the problems that need to be solved in the field of radio modulation recognition and the future research directions. Section 9 concludes the whole paper.