## 3 Methods
### Data Augmentation Methods for AMR  
Data augmentation is a set of techniques for artificially increasing the amount of data by generating new samples from existing data. This includes applying small transformations to data, combining data samples, and generating new data samples with deep learning models. Data augmentation is frequently used to prevent overfitting, improve model generalization, address class imbalances, and reduce the cost of collecting and labeling data. A number of traditional data augmentation methods have been successfully applied to computer vision and speech-related tasks, achieving SOTA performance. The basic linear data augmentation methods are rotation, scale, shear, and flip, while non-linear data augmentation methods include mosaic [19] and mixup [20]. Non-linear augmentation methods and their combinations are typically more effective than simple linear augmentation methods in tasks such as image classification and target detection. The same data augmentation methods that have been used successfully in the image domain are now widely used in speech. It is standard practice to first transform the raw speech signal into a Mel spectrogram before applying a data augmentation transform [41]. If an end-to-end architecture is used and the raw audio data are directly fed into a deep neural network model, widely used and proven methods for audio data augmentation include pitch shifting, time stretching, and random frequency filtering [21]. In the medical field, various data augmentation methods and combinations of methods for data from wearable sensors have been validated. These data augmentation methods are based on domain knowledge and thus enable efficient expansion of the data. Modulated signal data augmentation can be seen as the injection of a priori knowledge about the invariant properties of radio signal data for certain transformations. In realistic scenarios, the data used for model training are obtained from a limited number of scenarios, while the target application of the model may exist in different conditions, such as fluctuating channel environments. Therefore, the amount of data has a decisive impact on the performance of the model. On the other hand, deep learning models usually contain huge amounts of parameters in order to enhance their representational capabilities. If there is a mismatch between the amount of data and the size of the model, the model is prone to overfitting. Data augmentation, on the other hand, can expand the input space not covered by the data, thus implicitly increasing the amount of data and extending the diversity of the data, preventing the model from overfitting the training data, and improving the generalization ability of the deep learning model on the test data. However, the most critical issue facing practical applications is how to design data augmentation methods for the model so as to obtain greater improvements in the model's performance. This process relies on domain knowledge, as modulated signal data are a type of time series data, unlike electrocardiogram signal data in the medical field [42], which are not used to distinguish an event in a regular signal sequence but prefers a pattern of signal sample points over a period of time, which is difficult to detect intuitively in the absence of expert knowledge. Therefore, when designing augmentation methods for modulated signals, we must not only focus on the commonalities with other time-series data but also take into account the unique characteristics of modulated signals. In particular, a modulated signal sample consists of I and Q components, which are closely related at each point in time, as is evident from the APs representation of the signal, and therefore it may be difficult to obtain useful synthetic data by transforming the I or Q components alone. Based on this principle, we proposed four new and one combination of label-preserving transformations for modulated signals.