  With the rapid development of deep learning, automatic modulation recognition
(AMR), as an important task in cognitive radio, has gradually transformed from
traditional feature extraction and classification to automatic classification
by deep learning technology. However, deep learning models are data-driven
methods, which often require a large amount of data as the training support.
Data augmentation, as the strategy of expanding dataset, can improve the
generalization of the deep learning models and thus improve the accuracy of the
models to a certain extent. In this paper, for AMR of radio signals, we propose
a data augmentation strategy based on mixing signals and consider four specific
methods (Random Mixing, Maximum-Similarity-Mixing, $\theta-$Similarity Mixing
and n-times Random Mixing) to achieve data augmentation. Experiments show that
our proposed method can improve the classification accuracy of deep learning
based AMR models in the full public dataset RML2016.10a. In particular, for the
case of a single signal-to-noise ratio signal set, the classification accuracy
can be significantly improved, which verifies the effectiveness of the methods.
