Abstract
Existing convolutional neural networks, which are used for radio frequency fingerprints recognition, process time-sequenced IQ (in-phase and quadrature) signals as images directly, resulting in low recognition accuracy and high computation complexity. IQCNet(convolutional neural network structure based on IQ correlation features), an efficient convolutional network structure, was proposed. IQCNet firstly extracted IQ correlation features and time domain features, then obtained the average value of each channel features through adaptive average pooling, and finally used only one fully connected layer for classification. Experimental results under a variety of channel conditions show that IQCNet improves recognition accuracy greatly with lower computation complexity compared with traditional convolutional neural networks.
Translated title of the contribution | High-efficiency IQ convolutional network structure for radio frequency fingerprint identification |
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Original language | Chinese (Traditional) |
Journal | Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology |
Volume | 44 |
Issue number | 4 |
Pages (from-to) | 180-189 |
Number of pages | 10 |
ISSN | 1001-2486 |
DOIs | |
Publication status | Published - Aug 2022 |
Bibliographical note
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