Cooperative Spectrum Sensing Method Based on Deep Convolutional Neural Network

Jianxin GAI; Xianfeng XUE; Jingyi WU; Ruixiang NAN

doi:10.11999/JEIT201005

Volume 43 Issue 10

Oct. 2021

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Jianxin GAI, Xianfeng XUE, Jingyi WU, Ruixiang NAN. Cooperative Spectrum Sensing Method Based on Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2911-2919. doi: 10.11999/JEIT201005

Citation:

Jianxin GAI, Xianfeng XUE, Jingyi WU, Ruixiang NAN. Cooperative Spectrum Sensing Method Based on Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2911-2919. doi: 10.11999/JEIT201005

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Cooperative Spectrum Sensing Method Based on Deep Convolutional Neural Network

doi: 10.11999/JEIT201005

The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China

Funds: The National Natural Science Foundation of China (61501150), The Natural Science Foundation of Heilongjiang Province (QC2014C074), The Fundamental Research Funds for the Universities in Heilongjiang Province (2018-KYYWF-1656)

Received Date: 2020-11-30
Rev Recd Date: 2021-03-12

Available Online: 2021-03-25

Publish Date: 2021-10-18

Abstract

Abstract

The traditional spectrum sensing method of Convolutional Neural Network (CNN) has a simple network structure which limits the ability of feature extraction. To solve the problem of gradient disappearance, a cooperative spectrum sensing method based on Deep Convolutional Neural Network (DCNN) is proposed in this paper, in which shortcut connections are added to the CNN to realize the deeper network of input level identity radiation. This method transforms the spectrum sensing problem into the image binary classification problem, and performs normalized gray level processing on the covariance matrix of Quadrature Phase Shift Keying (QPSK) signal as the input of DCNN, which trains DCNN model through residual learning and extracts the deep image features of the two-dimensional grayscale image. The testing data is input into the trained model and spectrum sensing based on image classification is completed. The experimental results show that the proposed method has higher detection probability and lower false alarm probability than the traditional spectrum sensing method when the Signal to Noise Ratio (SNR) is low and multiple users collaborate in sensing.
- Cooperative spectrum sensing,
- Deep Convolutional Neural Network (DCNN),
- Residual learning,
- Covariance matrix

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References(20)

References

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