# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
###### Abstract  
With the continuous development of communication technology, the wireless communication environment becomes more and more complex with various intentional and unintentional signals. Radio signals are modulated in different ways. The traditional radio modulation recognition technology cannot recognize the modulation modes accurately. Consequently, the communication system has embraced Deep Learning (DL) models as they can automatically recognize the modulation modes and have better accuracy. This paper systematically summarizes the related contents of radio Automatic Modulation Recognition (AMR) based on DL over the last seven years. First, we summarize the current research status of modulation recognition and the necessity of AMR research based on DL. Then, we review current radio AMR methods based on DL. In addition, we also propose a network model of AMR based on Convolutional Neural Network (CNN) and prove its effectiveness. Finally, we highlight existing challenges and research directions of radio AMR based on DL. deep learning; automatic modulation recognition; neural network; radio signal +
Footnote †: journal: Applied  
scenarios. In the process of signal transmission, on one hand, the signal transmitted by the transmitter is usually affected by noise, multipath fading, center frequency offset, etc. ; on the other hand, the signal structure is distorted due to poor hardware design or crystal oscillator drifting, which makes it difficult to distinguish different modulation schemes. At this time, AMR plays a key role. This technology can automatically identify the modulation type of the signal, so as to obtain the information contained in the signal without knowing the system parameters. Its automation can greatly reduce the consumption of human resources. At the same time, it can greatly improve the accuracy of signal modulation recognition. In the early days, traditional modulation recognition mainly depended on manual work. The operator could judge the modulation mode of the signal by observing the time domain and frequency domain of the signal with the oscilloscope and other instruments. This manual method not only had large recognition error, but also long recognition time and could only be applied to limited modulation types. Later, with the development of modulation recognition technologies, there are two Automatic Modulation Classification (AMC) methods. One is the modulation recognition method based on the maximum likelihood ratio [4; 5; 6] and the basic idea of this method is hypothesis testing. First, the likelihood probability model is given to estimate the probabilities of different modulation modes. Then, the possible modulation modes are tested. Finally, the modulation type with the maximum likelihood probability is selected as the experimental result. This method can make the classification result optimal while ensuring the Bayesian minimum error criterion. However, this method needs a lot of computation and a priori information, so it cannot be used widely. Another is the modulation method based on feature extraction [7; 8; 9; 10; 11; 12; 13]. This method extracts the spectrum differences between different modulation types and classifies these features by constructing a classifier model, so as to obtain the modulation mode of the unknown signal. This method has low complexity. However, it relies on the selection of signal features. If the selected features are not distinguishable in the communication system, the classification effect will be very poor. Therefore, it is necessary to find an algorithm which has strong generalization ability and can automatically learn modulation features from sample data. Today, Machine Learning (ML) and DL show overwhelming advantages in fields such as computer vision [14], speech recognition [15], image processing [16] and robotics [17]. For ML, its learning algorithms need to be designed by expert engineers and are mainly aimed at manual extraction of engineering features.