## 2 Related Work  
In recent years, some deep learning-based methods have taken into account the inherent characteristics of radio signals and communication systems in modulation recognition. Owing to the excellent achievements of DL in the field of Computer Vision (CV), researchers are keen to transform the signal recognition problem into an image recognition problem. In [13], AlexNet was introduced for automatic modulation recognition. To address the issue of low modulation recognition accuracy at low SNR, a modulation recognition algorithm based on ResNet50 with multi-feature fusion was presented in [14]. VGG networks exhibited excellent performance in image classification by converting the sampled IQ signals into grayscale images for AMR as described in [15]. Although some DL-based methods are helpful, the computational cost is high. Neural networks such as MobileNet have the potential for low computational cost and high accuracy [16]. Ref. [17] proposed an end-to-end Bidirectional Long Short-term memory (Bi-LSTM) algorithm which possesses low computational complexity at low SNR. In [18], [PERSON] et al. presented a modulation recognition method based on convolutional long short-term deep neural networks (CLDNNs), which assimilated the advantages of CNN and LSTM networks and achieved higher accuracy than other simpler CNNs. The authors in [19] advocated using end-to-end complex-valued CNN and complex-valued LSTM instead of dual-channel real-valued networks for modulation recognition, known as the CVCNN-LSTM. The adaptive wavelet network (AWN), which combines adaptive wavelet decomposition and a channel attention mechanism, was proposed for the AMR task, aiming to address the deficiency of existing schemes in utilizing feature maps [20]. In [21], they employed a CNN and recurrent neural network (RNN) for frequency-hopping signal detection. The TFI was input into the model, achieving more satisfactory detection results. The authors of [22] proposed a multimodal fusion model by integrating different modal features, including time-frequency features, higher-order cumulants, instantaneous features, etc., in order to further enhance the modulation recognition performance. A blind waveform recognition method based on Choi-Williams distribution (CWD) was designed [23]. In [24], the time-frequency attention (TFA) mechanism was proposed, which generates an attention-refined feature map by learning feature representations to explicitly focus on crucial channel, frequency, and time information. Although the TFA mechanism increases the computational burden, it has similar inference time and fewer parameters, and it also improves the recognition accuracy. An algorithm to remove Gaussian noise was proposed [25], which was applied to denoise the IQ signal before generating the time-frequency image for radar modulation type identification. Ref. [26] proposed an antifrequency offset constellation diagram (AFO-CD). Meanwhile, constellation-specific RNN was designed to act as a classifier, which significantly improves the recognition performance. Similarly, ref. [27] utilized graph convolution for modulation recognition of RGB time-frequency images and achieved good accuracy. [PERSON] et al. proposed a recognition method for intrapulse modulation radar signals, which combines STFT transform to enhance the recognition rate at low SNR [28]. Likewise, ref. [29] introduced vision transformer (ViT) and vicinity vision transformer (VViT) based on STFT into modulation recognition. However, feature extraction-based methods are highly reliant on the expert's understanding and experience in signal processing. The frequently utilized features comprise instantaneous characteristics, high-order statistics, cyclic spectrum, etc. These makes them complex and less flexible [1]. The IQ-based method is not effective in recognizing the signal when it is subjected to severe noise interference or channel impairments.