## 2 Related Work  
Automatic modulation classification (AMC) plays a vital role in radio monitoring and spectrum management and has been widely applied in various communication systems, such as satellite communications and network communications. AMC was first proposed by researchers from Stanford University [36], and it aimed at overcoming the limitations of traditional manual identification methods and achieving high accuracy. Recent years have witnessed the development of different AMC methods, which can be generally categorized into three types: the likelihood-based method, the feature-based method, and the deep learning-based method (as shown in Figure 1). **Likelihood-based method. ** The modulation recognition method based on likelihood ratio testing (LRT) is carried out on top of composite hypothesis testing. First, the maximum likelihood function of the unknown signal and the optimal decision threshold are determined based on comprehensive analysis of signal characteristics. Then, the extracted statistics are compared with the thresholds to achieve modulation signal classification. The authors in [37] propose a modulation recognition method based on the Average Likelihood Ratio Test (ALRT), which shows potential in distinguishing between binary phase-shift keying (BPSK) and quadrature phase shift keying (QPSK) signals. In [38], a Generalized Likelihood Ratio Test (GLRT)-based AMC approach is proposed by combining power series with likelihood functions. It achieves comparable recognition performance to the average likelihood approach while reducing computational complexity and decreasing implementation difficulties. A Hybrid Likelihood Ratio Test (HLRT) classification approach, proposed in [39], both preserves the advantages of conventional algorithms and suppresses inter-symbol interference while maintaining reasonable computational complexity. Although likelihood-based methods can achieve optimal classification performance in the Bayesian sense, their practical application faces challenges due to the need to account for unknown parameters. As the number of unknown parameters increases, they become increasingly difficult to implement and computationally intensive. Figure 1: Automatic modulation classification methods. **Feature based method. ** This type of methods extracts key features from modulated signal samples and builds classifiers for modulation classification. As different types of signals are characterized by diverse features, adopting different types of features can significantly impact signal recognition accuracy. Typically, expertise-based features are categorized into time-domain features, cumulant-based features, spectral features, etc. To name a few, the authors in [40] extract and analyze nine distinct signal features, including amplitude, phase, and instantaneous frequency characteristics, and conduct modulation recognition based on these features. Ref. [41] utilizes fourth-order cumulants as signal features and employs a hierarchical model to progressively classify modulation types, including M-ary phase-shift keying (MPSK), M-ary quadrature amplitude modulation (MQAM), and M-ary pulse-amplitude modulation (MPAM). In [42], a hidden Markov model (HMM) approach is employed to analyze spectral correlation characteristics in the cyclic frequency domain. The feature-based methods can achieve relatively satisfactory recognition performance. However, they often require extensive preprocessing procedures and highly rely on feature extraction. When the received signal is contaminated by noise, the feature extraction process becomes less effective, leading to a degraded overall recognition performance. **Deep learning-based method.