# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 2 Traditional Methods of Modulation Recognition
### Feature-Based Recognition Algorithms  
The feature-based (FB) modulation identification method is a very important method in the development of AMR technology. This method does not need to strictly deduce the likelihood function of the signal, but only needs to be able to extract the representative features of different signals. The classification can be completed according to the corresponding features, and the classification accuracy (the classification accuracy here can be expressed as \(P_{cc}=S_{c}/S\times 100\%\), where \(S_{c}\) is the number of correctly classified samples, \(S\) is the given total sample size) can meet the requirements. Therefore, the feature-based modulation identification algorithm is often applied in practical engineering due to its low computational complexity and high accuracy. The flow of this method can be shown in Figure 3.  
As shown in Figure 3, the method first preprocesses the signal, then extracts the features of the signal, and finally uses the classifier to classify. In the whole recognition process, feature extraction and classifier design are two very important steps. Therefore, researchers must choose appropriate signal features and design good classifiers. There are many kinds of signal features, and different features have different characteristics. For example, the transient features of the signal do not require any parameter estimation and have low preprocessing requirements, but in fading channels they are prone to fluctuations, leading to limited learning ability of the classifier. The high-order statistics feature of the signal has strong anti-noise ability, has certain advantages in the extraction of weak features, and has better recognition under low signal-to-noise ratio. We summarize the now commonly used signal features as well as classifiers in Figure 4.  
The computational complexity of the feature-based recognition method is greatly reduced, and a satisfactory level of recognition accuracy can be achieved. Therefore, this method is favored by the majority of researchers. Since then, many scholars have been devoted to research on feature-based modulation recognition methods. [PERSON] [23] proposed a digital modulation identification method, which uses the histogram of signal amplitude, frequency, and differential phase, as well as characteristic functions such as amplitude variance and frequency variance and adopts the classification method of pattern recognition to automatically classify signals according to the nearest principle. The authors of [24] proposed to extract the signal's instantaneous features to identify the type of digital modulation. Later, to solve the problem of poor robustness of transient features, some scholars proposed using higher-order accumulations of signals [25; 26; 27], cyclic spectra [28; 29; 30], and higher-order spectra [31] as features and using machine learning algorithms to design classifiers to improve recognition accuracy. Among them, the most commonly used classifiers are as follows: K-Nearest Neighbor [32], support vector machine [25; 26; 30], random forest [33], and artificial neural network [34]. Although the combination of signal features and machine learning algorithms can achieve better recognition results, the complexity of classifier rule design in complex communication systems is still very high, the scalability is poor, and the recognition accuracy is relatively low in low signal-to-noise ratio environments. Therefore, the modulation identification methods based on machine learning still have great limitations. Accordingly, researchers still have to continue to improve the accuracy of AMR algorithms. According to the above-mentioned introduction concerning the likelihood recognition method (LB) and the feature (FB) recognition method, we summarize the advantages and disadvantages of these two methods in Table 1.  
Figure 3: Flowchart of the characteristic modulation identification method.