## 2 Related Works  
In [22], the authors used a phase-based short-time Fourier transform (STFT) to analyze radar signals and extract time-frequency features that emphasize phase characteristics. These features were then processed with a bidirectional long short-term memory (BiLSTM) network, which could model dependencies in both forward and backward time directions. The approach performed well, especially in low-SNR scenarios, showing how phase-based STFT combined with BiLSTM can effectively classify radar signals.  
In [23], the authors proposed a method for modulation classification that transforms received signals into signal constellation diagrams, representing key modulation characteristics visually. These diagrams were analyzed using a convolutional neural network (CNN), which extracted features automatically for classification. The approach showed high accuracy, even in noisy and low-SNR conditions, highlighting the practical benefits of using constellation diagrams and CNNs for signal recognition.  
In [24], the authors introduced an LSTM-guided modulation classification method for sub-Nyquist rate wideband spectrum sensing. The approach utilizes long short-term memory (LSTM) networks to classify modulations in signals sampled at sub-Nyquist rates, making it more efficient for wideband-spectrum-sensing applications. The approach was validated through experiments, showing strong performance in terms of both classification accuracy and computational efficiency.  
In [25], the authors proposed MCNet, a lightweight and efficient CNN architecture for automatic modulation classification. The model is designed to handle challenging channel conditions and noise levels while keeping computational requirements low. By focusing on effective feature extraction, MCNet achieves strong classification performance with reduced complexity.  
A recent study in [13] proposes an automatic modulation recognition (AMR) method for radiation source signals that leverages a two-dimensional data matrix and an improved residual neural network. The approach involves two key components: (1) a preprocessing technique that reconstructs intercepted time-series signals into 2D matrices to enhance task autonomy and simplify preprocessing, and (2) a deep residual network enhanced with depthwise convolutions and large convolutional kernels to improve feature extraction and recognition performance.  
The authors in [26] propose a deep convolutional neural network (CNN) architecture enhanced with a multi-task learning (MTL) scheme for automatic modulation recognition. The MTL framework is designed to address common classification challenges by training on related tasks simultaneously, particularly focusing on resolving confusion between similar modulation types.  
The study [27] proposes CGDNet, a recurrent fully connected deep neural network that combines a shallow CNN for spatial feature extraction, a GRU for modeling temporal dependencies, and a DNN for classification, effectively balancing accuracy and computational efficiency. Further improving robustness in low-SNR environments, Ref. [28] introduces a transformer-based semi-supervised framework leveraging contrastive learning and data augmentation (e.g., time warping), where a convolutional transformer encoderjointly learns local and global signal features, achieving state-of-the-art performance on RML2016 benchmarks.