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.

翻译：近年来，利用深度学习进行无线电调制识别已变得普遍。该方法能从大型数据集中自动提取高维特征，从而促进调制方案的准确分类。然而，在真实场景中，预先收集充足的训练数据可能并不可行。数据增强是一种用于增加训练数据集的多样性和数量、并减少数据稀疏性与不平衡性的方法。本文提出了一种数据增强方法，该方法涉及替换由离散小波变换分解得到的细节系数以重构信号，从而生成新样本并扩充训练集。我们采用不同的生成方法创建替换序列。仿真结果表明，我们提出的方法显著优于其他增强方法。