Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition
This work addresses data scarcity in cognitive radio for improved signal classification, but it is incremental as it applies existing augmentation concepts to a specific domain.
The paper tackles the problem of limited training data for deep learning-based automatic modulation recognition by proposing a data augmentation strategy that mixes radio signals, which improves classification accuracy on the RML2016.10a dataset, especially for single signal-to-noise ratio cases.
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, $θ-$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.