## 1 Introduction  
With extensive research and application of deep neural networks in various fields, the performance of deep learning-based automatic modulation recognition (AMR) has improved tremendously over traditional signal processing methods [1]. The key enablers of deep learning are the availability of large datasets, the advent of GPUs, and the continued development of deep network architectures [2]. AMR tasks based on deep learning cannot be performed without high-quality labeled data, which are required for both algorithmic research and practical deployment. The amount of data in existing open-source datasets is the same for all modulation types [1, 3, 4]. However, deep learning-based AMR tasks face three real-world challenges: (1) insufficient data; (2) imbalanced classes; and (3) high computational demands when computational power is limited. Sensor data are always skewed by class imbalance, i.e., a small number of classes have a large number of sample points, while other classes have only a few samples. There is also the possibility of addressing the low-data regime problem [5]. Data are crucial not only in communications but also in high-tech fields such as finance, automation, and blockchain [6, 7, 8]. Deep learning models struggle to achieve excellent performance when confronted with a class imbalance in a low-data regime, resulting in poor classification performance for minority classes with insufficient data. This has become a major challenge because the minority class has a higher cost of misidentification because it represents favorable samples that are rare or expensive in nature. A number of recent studies have focused on the problem of insufficient data and imbalanced classes, such as resampling and reweighting based on the sample size of each class. Resampling is achieved by increasing the weight of the minority class while decreasing the weight of the majority class. Upsampling is the most commonly used method in recent years [9; 10; 11; 12], which can increase the likelihood of overfitting and significantly increase training complexity, particularly at higher oversampling rates, making it difficult for classifiers to achieve good generalization performance in testing or in practical applications [13; 14]. The most successful application of this strategy, which sets the sample weights inversely proportional to the class frequencies, is in the field of target detection for balancing background classes and other classes [15]. Recent research on large, real-world long-tail datasets has revealed that this strategy performs poorly [16]. In contrast, data augmentation methods expand the dataset by transforming the original data to generate synthetic samples [17; 18; 19; 20; 21]. It is a simple and practical technique. It is more widely used than other methods because it can be effectively applied to various types of class imbalance and low-data regime problems. Although the straightforward application of data augmentation may exacerbate the class imbalance, the method is extremely effective at suppressing overfitting. Furthermore, data augmentation can be used in combination with class rebalancing and module improvement [22]. In this paper, we only focus on the effective data augmentation method for AMR from low-data imbalanced-class regime. The balancing method and model design that go with it are beyond the scope of this paper. Research into data augmentation methods for modulated signals has received little attention in comparison to computer vision and speech [18]. Existing research has shown that data augmentation can significantly improve deep learning model classification performance under class-balanced conditions. The main question at this point is how to create effective data augmentation methods and strategy combinations to improve modulation recognition performance in low-data regimes with class imbalance. Domainality, model and data distribution independence, and complexity must all be considered when developing data augmentation methods for modulated signals.