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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 719-720Tensors Method Based Method of RF Fingerprints...

Tensors Method Based Method of RF Fingerprints Identification

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Abstract:

Because of RF power amplifier’s nonlinearity and the discrete distribution of electronic components value, it is possible to form individual RF fingerprints. How to identify individual RF fingerprints is the key problem because received RF signals are weak to be affected by channel noises. In order to identify steady RF signal fingerprints, this paper proposed a tensors-based method for obtaining identification patterns from high order cummulants. Firstly, high order cummulants tensors of received RF signal were formed as distinguished patterns of RF Fingerprints. Secondly, these cummulants tensors were passed to classifiers as training data and testing data. The RF Fingerprint classifiers based on SVM and RBF networks are compared with their performance. Finally, the identification results of two classifiers were obtained. The results showed that the method is effective for RF Fingerprints Identification.

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Materials and Engineering Technology

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Periodical:

Applied Mechanics and Materials (Volumes 719-720)

Pages:

941-948

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.719-720.941

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Online since:

January 2015

Authors:

Zhi Ling Tang*, Si Min Li

Keywords:

RBF Network, RF Fingerprints, SVM, Tensor

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] Ureten O., Serinken N.: Wireless security through RF fingerprinting. Canadian Journal of Electrical and Computer Engineering, vol. 32, no. 1, pp.27-33(2007).

DOI: 10.1109/cjece.2007.364330

[2] Crystal B., Kevin R., Brya N., Mark H.: Wavelet Fingerprinting of Radio-Frequency Identification (RFID) Tags. IEEE Transactions on Industrial Electronics , vol. 59, no. 12, pp.4843-4850 (2012).

DOI: 10.1109/tie.2011.2179276

[3] Afolabi O., Kim K., Ahmad A.: On Secure Spectrum Sensing in Cognitive Radio Networks Using Emitters Electromagnetic Signature. In: IEEE Proceedings of 18th International Conference on Computer Communications and Networks, San Francisco, California, pp.1-5. IEEE Press, New York (2009).

DOI: 10.1109/icccn.2009.5235288

[4] Reising D. R., Temple M. A., Mendenhall M. J.: Improving intra-cellular security using air monitoring with RF fingerprints. In: IEEE Wireless Communications and Networking Conference (WCNC), pp.1-6 . IEEE Press, New York (2010).

DOI: 10.1109/wcnc.2010.5506229

[5] Polak A., Dolatshahi S., Goeckel D.: Identifying wireless users via transmitter imperfections. IEEE Journal on Selected Areas in Communications, vol. 29, no. 7, pp.1469-1479(2011).

DOI: 10.1109/jsac.2011.110812

[6] Saeed U. R., Kevin S., Colin C.: RF Fingerprint Extraction from the Energy Envelope of an Instantaneous Transient Signal. IEEE Communications Theory Workshop, Australian, pp.90-95 (2012).

DOI: 10.1109/ausctw.2012.6164912

[7] Ellis K., Serinken N.: Characteristics of radio transmitter fingerprints. Journal of Radio Science, vol. 36, no. 4, pp.585-597(2001).

DOI: 10.1029/2000rs002345

[8] Shaw D., Kinsner W.: Multi-fractal Modeling of Radio Transmitter Transients for Classification. In: Proceedings Conference on Communications, Power and Computing, pp.306-312 (1997).

DOI: 10.1109/wescan.1997.627159

[9] Klein R.W., Temple M.A., Mendenhall M. J.: Application of Wavelet Denoising to Improve OFDM-Based Signal Detection and Classification. Journal on Security and Communication Networks, Special Issue: Security in Next Generation Wireless Networks, vol. 2, no. 6, pp.71-82 (2009).

DOI: 10.1002/sec.115

[10] Klein R.W., Temple M.A., Mendenhall M. J.: Application of Wavelet-Based RF Fingerprinting to Enhance Wireless Network Security. Journal on Communication Networks, Special Issue: Secure Wireless Networking, vol. 11, no. 6, pp.544-555 (2009).

DOI: 10.1109/jcn.2009.6388408

[11] IRWIN O. Kennedy, MILIND M. Buddhikot, Nolan Keith E.: Radio Transmitter Fingerprinting: A Steady State Frequency Domain Approach. In: IEEE 68th Vehicular Technology Conference, pp.1-5, IEEE Press, New York (2008).

DOI: 10.1109/vetecf.2008.291

[12] Dominique S., Mairtin O., Anthony A. G.: RF Power Amplifier Behavioral Modeling. Cambridge University Press , London, pp.35-44 (2009).

[13] Swami A., Giannakis G.: Multichannel ARMA Processes. IEEE Transactions on Signal Processing, vol. 42, no. 4, pp.898-913 (1994).

DOI: 10.1109/78.285653

[14] Leonov V. P., Shiryaev A. N.: On the Technique of Computing Semi-Invariants. Theory of Probability Applications, vol. 4, pp.319-329 (1959).

[15] Liu C., Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, No. 5, pp.57-581 (2004).

DOI: 10.1109/tpami.2004.1273927

[16] Marco S., Lieven D. L., Johan A. K. S.: A kernel-based framework to tensorial data analysis. Neural Networks, vol. 24, no. 8, pp.861-874 (2011).

DOI: 10.1016/j.neunet.2011.05.011

[17] [Online] http: /www. crawdad. org/, (2008).