Small Sample Signal Modulation Recognition Algorithm Based on Support Vector Machine Enhanced by Generative Adversarial Networks Generated Data

XIE Zhidong; TAN Xin; YUAN Xinwang; YANG Gang; HAN Yu

doi:10.11999/JEIT220624

Volume 45 Issue 6

Jun. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(6): 2071-2080

XIE Zhidong, TAN Xin, YUAN Xinwang, YANG Gang, HAN Yu. Small Sample Signal Modulation Recognition Algorithm Based on Support Vector Machine Enhanced by Generative Adversarial Networks Generated Data[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2071-2080. doi: 10.11999/JEIT220624

Citation:

XIE Zhidong, TAN Xin, YUAN Xinwang, YANG Gang, HAN Yu. Small Sample Signal Modulation Recognition Algorithm Based on Support Vector Machine Enhanced by Generative Adversarial Networks Generated Data[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2071-2080. doi: 10.11999/JEIT220624

Citation:

XIE Zhidong, TAN Xin, YUAN Xinwang, YANG Gang, HAN Yu. Small Sample Signal Modulation Recognition Algorithm Based on Support Vector Machine Enhanced by Generative Adversarial Networks Generated Data[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2071-2080. doi: 10.11999/JEIT220624

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Small Sample Signal Modulation Recognition Algorithm Based on Support Vector Machine Enhanced by Generative Adversarial Networks Generated Data

doi: 10.11999/JEIT220624

College of National Defense Technology and Innovation, Academic of Military Science, Beijing 100071, China

Received Date: 2022-05-17
Rev Recd Date: 2022-10-07

Available Online: 2022-10-11

Publish Date: 2023-06-10

Abstract

Abstract

Focusing on solving the problem of small sample signal modulation recognition, the theoretical feasibility of using Support Vector Machine (SVM) for modulation recognition is investigated firstly; Secondly, based on statistical learning theory, a theoretical analysis of using Generative Adversarial Networks (GAN) generated data to enhance the classification ability of SVM is conducted; And finally, a Deep Convolutional Generative Adversarial Network based on Layer normalization (LDCGAN) is constructed , whose generated data has more obvious features than Deep Convolutional Generative Adversarial Networks (DCGAN) after mapping to a high-dimensional space, so the generated data is more conducive to the classification of SVM. The experiments verify that LDGAN generated data can achieve an effective enhancement of the classification ability of SVM under the condition of small samples.
- Modulation recognition,
- Generative Adversarial Networks (GAN),
- Support Vector Machine (SVM),
- Small samples

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References(25)

References

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