Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio

Wu Peng; Sun Bei; Su ShaojingWei JunyuZhao JinhuiWen Xudong

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Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio

机译：Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio

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With the development of artificial intelligence technology, deep learning has been applied to automatic modulation classification (AMC) and achieved very good results. In this paper, we introduced an improved deep neural architecture for implementing radio signal identification tasks, which is an important facet of constructing the spectrum-sensing capability required by software-defined radio. The architecture of the proposed network is based on the Inception-ResNet network by changing the several kernel sizes and the repeated times of modules to adapt to modulation classification. The modules in the proposed architecture are repeated more times to increase the depth of neural network and the model's ability to learn features. The modules in the proposed network combine the advantages of Inception network and ResNet, which have faster convergence rate and larger receptive field. The proposed network is proved to have excellent performance for modulation classification through the experiment in this paper. The experiment shows that the classification accuracy of the proposed method is highest with the varying SNR among the six methods and it peaks at 93.76% when the SNR is 14 dB, which is 6 percent higher than that of LSTM and 13 percent higher than that of MentorNet, Inception, and ResNet purely. Besides, the average accuracy from 0 to 18 dB of the proposed method is 3 percent higher than that of GAN network. It will provide a new idea for modulation classification aiming at distraction time signal.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2020年第43期|2678310.1-2678310.13|共13页
作者
Wu Peng; Sun Bei; Su ShaojingWei JunyuZhao JinhuiWen Xudong;
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Natl Univ Def Technol, Coll Intelligence Sci & Technol, Changsha 410073, Peoples R China;

Natl Univ Def Technol, Coll Intelligence Sci & Technol, Changsha 410073, Peoples R China|Hunan Univ Technol, Coll Elect & Informat Engn, Zhuzhou 412007, Peoples R China;

Natl Univ Def Technol, Teaching & Res Support Ctr, Changsha 410073, Peoples R ChinaChinese Peoples Liberat Army, 93920 Unit, Hanzhong, Peoples R China;

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Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio

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