基于生成对抗与卷积神经网络的调制识别方法

doi:10.12305/j.issn.1001-506X.2022.03.37

Abstract

Abstract:

Automatic modulation recognition occupies an important position in spectrum monitoring and cognitive radio. Aiming at the low recognition rate problem of existing modulation recognition algorithms under the condition of low signal to noise ratio, a digital signal modulation recognition method combined generative adversarial network (GAN) and convolutional neural network (CNN). After using the smooth pseudo Wigner-Ville distribution to convert the modulated signal into time-frequency images (TFIs), the residual dense block (RDB) structure is embeded in the classic GAN network to guarantees the denosing and repairmen of TFIs. By fine-tuning the classic residual networkl (ResNet) model of CNN network, the recognition and classification of TFIs is satisfied. The simulation results show that the proposed method effectively reduces the interference of noise on TFIs and improves the recognition performance under the condition of low signal to noise ratio.

Key words: automatic modulation recognition, time-frequency distribution, convolutional neural network (CNN), generative adversarial network (GAN), residual dense block

CLC Number:

TN911.3

Kai SHAO, Miaomiao ZHU, Guangyu WANG. Modulation recognition method based on generative adversarial andconvolutional neural network[J]. Systems Engineering and Electronics, 2022, 44(3): 1036-1043.

Figures/Tables 12

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Modulation recognition method based on generative adversarial andconvolutional neural network

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