MobileNet for Differential Constellation Trace Figure

Radio frequency fingerprint technology is of great significance to the security of the Internet of Things system. When the signal uses I/Q modulation, the demodulated signal can be drawn on a two-dimensional plane, that is, constellation diagram. Slight differences in the manufacture and use of transmitters can cause the constellation to shift. Differential Constellation Trace Figure (DCTF) with device characteristics can be generated by special differential processing of demodulation signal. In this paper, the DCTFs of QPSK signal are used as radio frequency fingerprints. Radio frequency fingerprints are classified using MobileNet, a lightweight network that can be used for mobile and embedded devices. In the final simulation experiment, the accuracy of MobileNet V2 and MobileNet V3 are both over 95% when SNR is low. Comparing with the accuracy of CNN, MobileNet V2 and MobileNet V3 are better choices.

[1]  Srdjan Capkun,et al.  Physical-Layer Identification of Wireless Devices , 2011 .

[2]  Quoc V. Le,et al.  Searching for MobileNetV3 , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[3]  Michel Barbeau,et al.  Enhancing intrusion detection in wireless networks using radio frequency fingerprinting , 2004, Communications, Internet, and Information Technology.

[4]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[5]  Zhaoning Zhang,et al.  Fd-Mobilenet: Improved Mobilenet with a Fast Downsampling Strategy , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[6]  Harold H. Szu,et al.  Novel identification of intercepted signals from unknown radio transmitters , 1995, Defense, Security, and Sensing.

[7]  Michel Barbeau,et al.  DETECTION OF TRANSIENT IN RADIO FREQUENCY FINGERPRINTING USING SIGNAL PHASE , 2003 .

[8]  Keith E. Nolan,et al.  Radio Transmitter Fingerprinting: A Steady State Frequency Domain Approach , 2008, 2008 IEEE 68th Vehicular Technology Conference.

[9]  Marco Gruteser,et al.  Wireless device identification with radiometric signatures , 2008, MobiCom '08.

[10]  Yashashree A. Jakhade,et al.  MobileNets for flower classification using TensorFlow , 2017, 2017 International Conference on Big Data, IoT and Data Science (BID).

[11]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[12]  Xuehong Sun,et al.  Diseases and Pests Identification of Lycium Barbarum Using SE-MobileNet V2 Algorithm , 2019, 2019 12th International Symposium on Computational Intelligence and Design (ISCID).

[13]  Dionis A. Padilla,et al.  Differentiating Atopic Dermatitis and Psoriasis Chronic Plaque using Convolutional Neural Network MobileNet Architecture , 2019, 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ).