The New Algorithm for The Blind Extraction of The Radio Frequency Fingerprint Using the Specific Features of High-Power Amplifier and Local Oscillator

Document Type : Original Article

Authors

1 PhD student, Imam Hossein University, Tehran, Iran

2 Professor, Amirkabir University of Technology, Tehran, Iran

3 Assistant Professor, Imam Hossein University, Tehran, Iran

4 Associate Professor, Imam Hossein University, Tehran, Iran

Abstract

Recently, the radio frequency fingerprint (RFF) has received attention in applications such as specific emiiter identification, detection of deception in navigation signals and detection of intrusion in wireless networks. The RFF is caused by the non-ideal manufacturing of the transmitter components. This effect appears as unintentional modulation in the output of the transmitter and its extraction can be considered as a solution of mentioned applications; Therefore, it is important to provide a method for extracting the RFF, using realistic modeling of the transmitter components. For this purpose, in this article, the combined effects of the power amplifier and local oscillator are considered as the fingerprint of the transmitter. Then, two blind algorithms based on the transmitter output signal are presented to extract the amplifier phase characteristic and the local oscillator phase noise. In the first algorithm, the phase function of the power amplifier in the presence of phase noise is estimated using the M’th order moment of the transmitter output signal. Then the power characteristic of the transmitter's local oscillator noise phase is obtained by blind estimation of its autocorrelation function. At the end, the results of the performance of the algorithms in the simulations are examined and it is shown that only for 1.5dB difference in power amplifier saturation power and 2dB difference in phase noise amount, two transmitters with the same modulations and frequencies can be separated with 98% accuracy in signal-to-noise ratio(SNR) equal to 10dB, where this precision is achievable in the recent works at 20dB SNR.

Keywords

Smiley face

References

[1]   S. Liu, X. Yan, P. Li, X. Hao, and K. Wang, “Radar Emitter Recognition Based on SIFT Position and Scale Features,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 65, no. 12, pp. 2062–2066, 2018, doi: 10.1109/TCSII.2018.2819666.
[2]   J. Sun, G. Xu, W. Ren, and Z. Yan, “Radar emitter classification based on unidimensional convolutional neural network,” IET Radar, Sonar Navig., vol. 12, no. 8, pp. 862–867, 2018, doi: 10.1049/iet-rsn.2017.0547.
[3]   L. Peng, A. Hu, J. Zhang, Y. Jiang, J. Yu, and Y. Yan, “Design of a Hybrid RF Fingerprint Extraction and Device Classification Scheme,” IEEE Internet Things J., vol. 6, no. 1, pp. 349–360, 2019, doi: 10.1109/JIOT.2018.2838071.
[4]   T. J. Bihl, K. W. Bauer, and M. A. Temple, “Feature Selection for RF Fingerprinting with Multiple Discriminant Analysis and Using ZigBee Device Emissions,” IEEE Trans. Inf. Forensics Secur., vol. 11, no. 8, pp. 1862–1874, 2016, doi: 10.1109/TIFS.2016.2561902.
[5]   W. Wang, Z. Sun, S. Piao, B. Zhu, and K. Ren, “Wireless Physical-Layer Identification: Modeling and Validation,” IEEE Trans. Inf. Forensics Secur., vol. 11, no. 9, pp. 2091–2106, 2016, doi: 10.1109/TIFS.2016.2552146.
[6]   E. Kupershtein, M. Wax, and I. Cohen, “Single-site emitter localization via multipath fingerprinting,” IEEE Trans. Signal Process., vol. 61, no. 1, pp. 10–21, 2013, doi: 10.1109/TSP.2012.2222395.
[7]   J. Lu and X. Xu, “Multiple-antenna emitters identification based on a memoryless power amplifier model,” Sensors (Switzerland), vol. 19, no. 23, Dec. 2019, doi: 10.3390/s19235233.
[8]   D. Roy, T. Mukherjee, and M. Chatterjee, “Machine Learning in Adversarial RF Environments,” IEEE Commun. Mag., vol. 57, no. 5, pp. 82–87, 2019, doi: 10.1109/MCOM.2019.1900031.
[9]   M. Aziz, M. Vejdani Amiri, M. Helaoui, and F. M. Ghannouchi, “Statistics-based approach for blind post-compensation of modulator’s imperfections and power amplifier nonlinearity,” IEEE Trans. Circuits Syst. I Regul. Pap., vol. 66, no. 3, pp. 1063–1075, 2019, doi: 10.1109/TCSI.2018.2877940.
[10] A. Ali and G. Fischer, “The Phase Noise and Clock Synchronous Carrier Frequency Offset based RF Fingerprinting for the Fake Base Station Detection,” 2019, doi: 10.1109/WAMICON.2019.8765471.
[11] A. C. Polak and D. L. Goeckel, “Wireless Device Identification Based on RF Oscillator Imperfections,” IEEE Trans. Inf. Forensics Secur., vol. 10, no. 12, pp. 2492–2501, 2015, doi: 10.1109/TIFS.2015.2464778.
[12] Z. Zhu, X. Huang, M. Caron, and H. Leung, “A blind AM/PM estimation method for power amplifier linearization,” IEEE Signal Process. Lett., vol. 20, no. 11, pp. 1042–1045, 2013, doi: 10.1109/LSP.2013.2280394.
[13] M. Valkama, M. Renfors, and V. Koivunen, “Blind signal estimation in conjugate signal models with application to I/Q imbalance compensation,” IEEE Signal Process. Lett., vol. 12, no. 11, pp. 733–736, 2005, doi: 10.1109/LSP.2005.856891.
[14] C. Zhao, M. Huang, L. Huang, X. Du, and M. Guizani, “A robust authentication scheme based on physical-layer phase noise fingerprint for emerging wireless networks,” Comput. Networks, vol. 128, pp. 164–171, 2017, doi: 10.1016/j.comnet.2017.05.028.
[15] L. Anttila, M. Valkama, and M. Renfors, “Gradient-based blind iterative techniques for I/Q imbalance compensation in digital radio receivers,” 2007, doi: 10.1109/spawc.2007.4401299.
[16] M. W. Liu and J. F. Doherty, “Nonlinearity estimation for specific emitter identification in multipath channels,” IEEE Trans. Inf. Forensics Secur., vol. 6, no. 3 PART 2, pp. 1076–1085, 2011, doi: 10.1109/TIFS.2011.2134848.
[17] X. Hong, S. Chen, Y. Gong, and C. J. Harris, “Nonlinear equalization of Hammerstein OFDM systems,” IEEE Trans. Signal Process., vol. 62, no. 21, pp. 5629–5639, 2014, doi: 10.1109/TSP.2014.2355773.
[18] D. Wang, A. Hu, Y. Chen, Y. Wang, and X. You, “An ESPRIT-based approach for RF fingerprint estimation in multi-antenna OFDM systems,” IEEE Wirel. Commun. Lett., vol. 6, no. 6, pp. 702–705, 2017, doi: 10.1109/LWC.2017.2731951.
[19] Y. Yuan, Z. Huang, H. Wu, and X. Wang, “Specific emitter identification based on Hilbert-Huang transform-based time-frequency-energy distribution features,” IET Commun., vol. 8, no. 13, pp. 2404–2412, Sep. 2014, doi: 10.1049/iet-com.2013.0865.
[20] S.Talati and M. R. Hassani Ahangar, “Radar Data Processing using a Combination of Principal  Component Analysis Methods and Self-Organizing and  Digitized Neural Networks of the Learning Vector” Journal of Electronical & Cyber Defence, vol. 9, no. 2, Serial no.34, 2021.)In Persian (
[21] N. J. Kasdin, “Discrete Simulation of Colored Noise and Stochastic Processes and 1/fα Power Law Noise Generation,” Proc. IEEE, vol. 83, no. 5, pp. 802–827, 1995, doi: 10.1109/5.381848.
[22] R. Corvaja, E. Costa, and S. Pupolin, “Analysis of M-QAM-OFDM transmission system performance in the presence of phase noise and nonlinear amplifiers,” in 1998 28th European Microwave Conference, EuMC 1998, 1998, vol. 1, pp. 481–486, doi: 10.1109/EUMA.1998.338036.
[23]   M. K. M. Fadul, D. R. Reising, T. D. Loveless, and A. R. Ofoli, “RF-DNA Fingerprint Classification of OFDM Signals Using a Rayleigh Fading Channel Model,” in IEEE Wireless Communications and Networking Conference, WCNC, 2019, vol. 2019-April, doi: 10.1109/WCNC.2019.8885421.
Electronic and Cyber Defense
Volume 11, Issue 1 - Serial Number 41
No. 41, Spring
May 2023
Pages 57-65

Files

History

  • Receive Date: 19 February 2022
  • Revise Date: 26 September 2022
  • Accept Date: 24 December 2022
  • Publish Date: 22 May 2023

Share

How to cite

Statistics