## 1 Introduction  
Underwater acoustic (UWA) communication often employs modulation techniques such as Multiple Frequency Shift Keying (MFSK), Multiple Phase Shift Keying (MPSK), Direct Sequence Spread Spectrum (DSSS), and Orthogonal Frequency Division Multiplexing (OFDM). Compared with active sonar pulse signals, UWA communication signals are more complex and diverse, with greater differences in signal characteristics and feature parameters [1, 2, 3]. Traditional UWA reconnaissance technologies are difficult to directly apply to the detection of UWA communication signals [4], mainly because UWA communication signal reconnaissance also involves signal-modulation-style recognition and classification. The differences between the modulation recognition of underwater acoustic communication signals and that of radio signals mainly lie in the channel environment, which means that identifying modulation patterns for UWA acoustic signals requires considering far more factors than radio signals. For example, the propagation speed of sound waves in water is relatively low (approximately 1500 m/s), resulting in significant delays in received signals (about 66 ms/km) [5]. Against this background, how to effectively design a modulation recognition method to address the impacts of underwater acoustic channels has become an urgent problem to solve. The modulation recognition of UWA signals is divided into traditional methods and those based on deep learning. Traditional methods are further divided into pattern recognition methods based on hypothesis testing and feature extraction. Compared with hypothesis testing methods, feature extraction methods do not rely on prior knowledge of the signal, so they are widely used in non-cooperative scenarios. [PERSON] et al. first proposed a modulation recognition method based on higher-order statistics (HOS) [6]. This method made up for the shortcomings of the traditional maximum-likelihood algorithm, such as extremely high computational complexity and poor robustness to phase and frequency offsets. It also solved the problem that the feature classification methods at that time relied on a large number of samples or complex pre-processing. However, because the variance of the fourth-order statistics estimation of this method will increase at a low signal-to-noise ratio (SNR), the decision boundaries between similar sub-classes become blurred, and there is a limitation of an increased probability of misclassification. [PERSON] et al. proposed a digital modulation signal classification method based on time-frequency representation (TFR) analysis and peak detection [7]. This method solved the problem that traditional HOS methods have an insufficient ability to distinguish multi-peak signals such as FSK and OFDM. However, the classification of this method highly depends on the setting of the peak detection threshold. When the noise is too large, it may lead to the misjudgment of peaks, which in turn affects the classification accuracy. Deep learning shows significant advantages by integrating traditional features and end-to-end learning. [PERSON] et al. proposed a recognition method based on CNN [8]. Additionally, the deep CNN model proposed by [PERSON] et al. [9] and the CNN model with optimized hyperparameters proposed by [PERSON] et al. [10] have all addressed the errors caused by manually designed decision criteria. However, constrained by the characteristics of their network architectures, these methods are prone to overfitting in small-sample scenarios and complex channels with fragmented features. Meanwhile, they exhibit limited robustness against low-SNR conditions and phase-modulated signals. With the emergence of the Long Short-Term Memory (LSTM) network, many people have started to introduce it into modulation recognition. [PERSON] et al. proposed a learning framework based on an LSTM denoising autoencoder [11], which can automatically extract stable features from noisy radio signals. The classification accuracy of modulation signals such as 8 PSK reaches 90% at a SNR of 8 dB.