Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866916458113531904 |
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| author | Xu, Xinjie Chen, Zhuangzhi Xu, Dongwei Zhou, Huaji Yu, Shanqing Zheng, Shilian Xuan, Qi Yang, Xiaoniu |
| author_facet | Xu, Xinjie Chen, Zhuangzhi Xu, Dongwei Zhou, Huaji Yu, Shanqing Zheng, Shilian Xuan, Qi Yang, Xiaoniu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_03737 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition Xu, Xinjie Chen, Zhuangzhi Xu, Dongwei Zhou, Huaji Yu, Shanqing Zheng, Shilian Xuan, Qi Yang, Xiaoniu Signal Processing Machine Learning 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. |
| title | Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2204.03737 |