A RF Fingerprint Recognition Method Based on Deeply Convolutional Neural Network

被引:0
作者
Zong, Lei [1 ]
Xu, Chen [1 ]
Yuan, HongLin [1 ]
机构
[1] Nantong Univ, Sch Informat Sci & Technol, Nantong, Peoples R China
来源
PROCEEDINGS OF 2020 IEEE 5TH INFORMATION TECHNOLOGY AND MECHATRONICS ENGINEERING CONFERENCE (ITOEC 2020) | 2020年
关键词
radio frequency (RF) fingerprints; Convolutional neural networks (CNN); Deep learning; short-time Fourier transform (STFT);
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Radio frequency (RF) fingerprinting is a process of identifying a device or annunciator. Because of the specificity of the wireless transmitting circuit and the imperfections of the components that make up the transmitting circuit, it is possible to extract the RF fingerprint used to identify the wireless transmitter from the wireless signal. At present, the traditional RF fingerprint identification method is performed based on a preset determination formula, and there are problems such as high prior information requirement and limited application range. To solve these problems, this paper proposes a RF fingerprint recognition method based on convolutional neural network (CNN). The research focuses on three aspects: RF fingerprint extraction, convolutional neural network design and wireless transmitter identification and verification. The result proves that this method can complete the wireless transmitter identification well.
引用
收藏
页码:1778 / 1781
页数:4
相关论文
共 10 条
  • [1] Abadi M., 2016, TENSORFLOW LARGE SCA
  • [2] [Anonymous], 2012, ELECT TEST
  • [3] Bo Deng, 2019, ELECT TECHNOLOGY SOF, P67
  • [4] Hao Li, 2020, COMPUTER ENG, P1, DOI [10.19678/j.issn.1000-3428.0056093, DOI 10.19678/J.ISSN.1000-3428.0056093]
  • [5] Panayotov V, 2015, INT CONF ACOUST SPEE, P5206, DOI 10.1109/ICASSP.2015.7178964
  • [6] Rehman KU, 2012, ACTUAL PROBL ECON, P90
  • [7] Szegedy C, 2017, AAAI CONF ARTIF INTE, P4278
  • [8] Szegedy C, 2015, PROC CVPR IEEE, P1, DOI 10.1109/CVPR.2015.7298594
  • [9] Wang Xin, 2018, RES RADIO FREQUENCY
  • [10] Yuan Honglin, 2009, Journal of Southeast University (Natural Science Edition), V39, P230