Spread Spectrum and Conventional Modulation Signal Recognition Method Based on Generative Adversarial Network and Multi-modal Attention Mechanism

WANG Huahua; ZHANG Ruizhe; HUANG Yonghong

doi:10.11999/JEIT230518

Volume 46 Issue 4

Apr. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(4): 1212-1221

WANG Huahua, ZHANG Ruizhe, HUANG Yonghong. Spread Spectrum and Conventional Modulation Signal Recognition Method Based on Generative Adversarial Network and Multi-modal Attention Mechanism[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1212-1221. doi: 10.11999/JEIT230518

Citation:

WANG Huahua, ZHANG Ruizhe, HUANG Yonghong. Spread Spectrum and Conventional Modulation Signal Recognition Method Based on Generative Adversarial Network and Multi-modal Attention Mechanism[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1212-1221. doi: 10.11999/JEIT230518

Citation:

WANG Huahua, ZHANG Ruizhe, HUANG Yonghong. Spread Spectrum and Conventional Modulation Signal Recognition Method Based on Generative Adversarial Network and Multi-modal Attention Mechanism[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1212-1221. doi: 10.11999/JEIT230518

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Spread Spectrum and Conventional Modulation Signal Recognition Method Based on Generative Adversarial Network and Multi-modal Attention Mechanism

doi: 10.11999/JEIT230518

WANG Huahua^{1, 2},
ZHANG Ruizhe^{1, 2
,
,},
HUANG Yonghong³

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of Mobile Communication Technology, Chongqing 400065, China
3.
School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61701063), The Natural Science Foundation of Chongqing (cstc2021jcyj-msxmX0454)

Received Date: 2023-05-30
Rev Recd Date: 2023-12-26

Available Online: 2023-12-29

Publish Date: 2024-04-24

Abstract

Abstract

Considering the low classification accuracy of spreading and conventional modulated signals under low signal-to-noise ratio conditions, a multimodal attention mechanism signal modulation recognition method based on Generative Adversarial Network (GAN) and Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) network is proposed. Firstly, the Time-Frequency Images (TFIs) of the to-be-recognized signals are generated and the noise reduction process of TFIs is realized by using GAN; Secondly, the In-phase and Quadrature data (I/Q data) of the signals with TFIs are used as model inputs, and the CNN-based TFIs recognition branch and the LSTM-based I/Q data recognition branch are built; Finally, an attentional mechanism is added to the model to enhance the role of important features in I/Q data and TFIs in the determination of classification results. The experimental results show that the proposed method effectively improves the overall classification accuracy by 2% to 7% compared with the unimodal recognition model and other baseline models, and possesses stronger feature expression capability and robustness under low signal-to-noise ratio conditions.
- Deep learning,
- Automatic Modulation Recognition(AMR),
- Generate Adversarial Network(GAN),
- Multi-modal features,
- Time-frequency distribution

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References(16)

References

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