## 1 Introduction
Signal samples differ from image data in terms of intrinsic characteristics, the most notable of which are temporal dependency and spatial dependency, where spatial correlation refers to the correlation between the I and Q components [23]. Signal samples can be transformed in both the time and frequency domains, so that corresponding data augmentation methods can be designed in the transformed domains, but the temporal complexity of these transformations needs to be considered [24]. There are only two effective augmentation methods in the current study, namely rotation and flip [18], and further research is needed to design more effective methods. Existing data augmentation methods have yet to be tested for adaptability to different models and datasets. Data augmentation methods for signal data in simple channel environments, for example, may be ineffective for data in complex channel environments. Data augmentation methods for recurrent neural networks (RNNs) might not work for convolutional neural networks (CNNs), and augmentation methods for frequency domain data might not work for time domain data. Existing research has not investigated data augmentation methods that are effective for multiple types of models. As a result, selecting and combining augmentation algorithms is challenging. Despite numerous works devoted to providing optimal recognition accuracy, model design solutions, and data processing means for AMR tasks, current research on these tasks suffers from the following problems: (1) There has not been enough attention paid to the problems of insufficient data and imbalanced classes, which are prevalent in practical applications; (2) There are many methods used to deal with the aforementioned problems in the image domain, but they are all difficult to transfer directly to modulated signal classification tasks. (3) Existing data augmentation methods can vary significantly in their performance for different models. In this paper, we focus on the problems of AMR tasks in the low-data imbalanced-class regime. Our approach is to use automated data augmentation techniques. The idea is to design a set of individual data transformations based on the characteristics of the modulated signal. The augmentation sequence for the data samples is then randomly selected from this set of transformations. We present four new augmentation transformations and a combination method based on radio signal characteristics: channel shuffle, inversion, split and permutation, scaling, and flip and channel shuffle. In experiments, empirical results on several datasets with unbalanced classes and small sample sizes show that our model significantly outperforms existing state-of-the-art(SOTA) methods. Furthermore, extensive validation experiments and ablation studies can validate the aforementioned preliminary discovery. Our approach differs from previous work in the following ways: (1) When compared to RandAugment data augmentation, our approach does not require setting hyperparameters or grid search; additionally, RandAugment targets image data and SigAugment targets communication signal data; (2) When compared to existing individual data augmentation methods, SigAugment automatically selects augmentation sequences during training, which is more adaptive to the model and data; (3) When compared to existing resampling methods, our method implements online data transformation with little to no additional training overhead and is better at combating overfitting and improving model generalisation performance. We summarize the key contributions of this paper as follows.