## 2 Related Works  
Due to the accumulation of large data volumes and the widespread use of deep neural networks, deep learning-based AMR methods have outperformed traditional methods in terms of performance. Existing research has concentrated on developing deep neural network models based on open-source datasets and conducting studies on improving recognition accuracy, real-time performance, and the trade-off between the two [1; 3; 4; 23; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. Studies on the representation of radio signal data have also begun, which will provide additional domain knowledge aspects for deep learning-based automatic modulation identification methods [24]. Furthermore, data augmentation techniques have been shown to improve model classification accuracy [18; 30]. In practice, however, AMR as a time series classification task is frequently subject to class imbalance and a low-data regime problem. When using small sample sizes, recent studies have used few-shot learning techniques to solve the modulated signal recognition problem [5]. In contrast, little attention has been paid to the study of class imbalance in modulated signal data. In contrast, the class imbalance problem has received extensive attention and discussion in the fields of computer vision, speech, and text [13; 14; 16; 35; 36]. The synthetic minority oversampling technique (SMOTE) for oversampling to artificially mitigate the imbalance is a conventional approach to solving the imbalance classification problem [37]. This method is widely used, and there are numerous variations of it [9; 10; 11; 12].  
However, the resampling strategy may alter the distribution of the original data, compromising the model's performance. Over-sampling, in particular, generates a large amount of redundant data, reducing model training efficiency, and the strategy overfits minority classes, making it difficult to transfer the model's excellent performance on the training set to the test data, often resulting in poor generalization performance on very imbalanced data. On the other hand, downsampling can result in significant information loss in the sample and even underfitting. The cost-sensitive reweighting technique is another alternative. Reweighting is the process of assigning different weights to different classes or samples, primarily to solve the class imbalance problem by reweighting the losses of different classes [15; 35]. Samples from minority classes have a higher loss than samples from majority classes because the features learned in minority classes tend to be weaker. This can easily lead to the model overfitting the minority class [36]. A side effect of assigning higher weights to hard samples is the focus on noisy data. Assigning high weights to samples in modulation recognition tasks, which frequently contain a large number of samples with very low signal-to-noise ratios (SNRs), may reduce overall recognition performance. Other approaches are discussed in the recent review [13].  
Data augmentation techniques are a simpler and more effective approach that is widely used for tasks such as classification, target detection, and speech recognition [18; 19; 20; 21; 30; 38; 39], and they are especially effective for class imbalance and low-data regime problems [40]. To the best of our knowledge, rotation and flip are effective modulation signal augmentation methods [18], and it has been experimentally demonstrated that data augmentation methods can achieve higher recognition accuracy in low-data regimes than when no data augmentation methods are available for the entire dataset. Modulated signal data augmentation for imbalanced-class and low-data regimes, on the other hand, has not been thoroughly investigated.