  The use of deep learning for radio modulation recognition has become
prevalent in recent years. This approach automatically extracts
high-dimensional features from large datasets, facilitating the accurate
classification of modulation schemes. However, in real-world scenarios, it may
not be feasible to gather sufficient training data in advance. Data
augmentation is a method used to increase the diversity and quantity of
training dataset and to reduce data sparsity and imbalance. In this paper, we
propose data augmentation methods that involve replacing detail coefficients
decomposed by discrete wavelet transform for reconstructing to generate new
samples and expand the training set. Different generation methods are used to
generate replacement sequences. Simulation results indicate that our proposed
methods significantly outperform the other augmentation methods.
