Minhong Sun This email address is being protected from spambots. You need JavaScript enabled to view it.1, Tiancheng Xu1, Hongchen Guo1 and Hua Zhong1

1Department of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, P.R. China


 

Received: April 29, 2016
Accepted: March 11, 2018
Publication Date: June 1, 2018

Download Citation: ||https://doi.org/10.6180/jase.201806_21(2).0014  

ABSTRACT


In recent years, the identification of the same-model wideband wireless transmitter manufactured by a same manufacturer has emerged as a big challenge. In this paper, a model-based approach is proposed for the identification of the same type wideband wireless transmitter. A Hammerstein Wiener model is adopted for modeling the wideband wireless transmitter and an improved genetic algorithm is proposed for identifying the model.The estimated model parametersare taken as a feature vector for the identification of the wideband wireless transmitter. The simulation results verify the effectiveness of the proposed method. Moreover, the improved genetic algorithm achieves better estimation precision and higher identification rate than the basic genetic algorithm, the classic least squares iteration method, the AWPSO and the neural network algorithm.


Keywords: Transmitter Identification, System Identification, Hammerstein-Wiener Model, Genetic Algorithm


REFERENCES


  1. [1] Rehman, S. U., Sowerby, K. W. and Coghill, C., “Radio-frequency Fingerprinting for Mitigating Primary User Emulation Attack in Low-end Cognitive Radios,” IET Communications, Vol. 8, No. 8, pp. 12741284 (2014). doi: 10.1049/iet-com.2013.0568
  2. [2] Lei, Y. K., “Individual Communication Transmitter Identification Using Correntropy-based Collaborative Representation,” International Congress on Image and Signal Processing, Biomedical Engineering and Informatics. IEEE, pp. 11941200 (2017).
  3. [3] Wen, H., Li, S. Q., Zhu, X. P. and Zhou, L., “A Framework of the PHY-layer Approach to Defense Against Security Threats in Cognitive Radio networks,” IEEE Network, Vol. 27, No. 3, pp. 3439 (2013). doi: 10. 1109/MNET.2013.6523806
  4. [4] Zhang, Y., Sun, B. F. and Xiao, J., “Fingerprint Identification Based on Wavelet Transform,” Aeronautical Computing Technique, Vol. 1 (2013). (Chinese) doi: 10.3969/j.issn. 1671-654x.2013.01.001
  5. [5] Liu, Y. Y., Zheng, L. H. and Liu, H. B., “Individual Transmitter Identification Based on New Feature Parameter of Signal Envelope,” Radio Communications Technology, Vol. 4 (2014). (Chinese) doi: 10.3969/j. issn.1003-3114.2014. 04.022
  6. [6] Liu, M. W. and Doherty, J. F., “Non linearity Estimation for Specific Emitter Identification in Multipath Channels,” IEEE Transactions on Information Forensics and Security, Vol. 6, No. 3, pp. 10761085 (2011). doi: 10.1109/SARNOF.2009.4850327
  7. [7] Taringou, F., Hammi, O. and Srinivasan, B., “Behaviour Modelling of Wideband RF Transmitters Using Hammerstein-Wiener models,” Circuits Devices & Systems Iet, Vol. 4, No. 4, pp. 282–290 (2010). doi: 10. 1049/iet-cds.2009.0258
  8. [8] Mao, Y. and Ding, F., “A Novel Data Filtering Based Multi-innovation Stochastic Gradient Algorithm for Hammerstein Nonlinear Systems,”Digital Signal Processing, Vol. 46, pp. 215225 (2015). doi: 10.1016/j.dsp.2015.07.002
  9. [9] Hammi, O., Kedir, A. M. and Ghannouchi, F. M., “Nonuniform Memory Polynomial Behavioral Model for Wireless Transmitters and Power Amplifiers,” Microwave Conference Proceedings (APMC), 2012 AsiaPacific. IEEE, pp. 836–838 (2012).
  10. [10] Zhou, L., Li, X. and Pan, F., “Gradient-based Iterative Identification for Wiener Nonlinear Systems with Nonuniform Sampling,”Nonlinear Dynamics, Vol. 76, No. 1, pp. 627634 (2014). doi: 10.1007/s11071-0131156-5
  11. [11] Abinayadhevi, P. and Prasad, S. J. S., “Identification of pH Process Using Hammerstein-Wiener Model,” Intelligent Systems and Control (ISCO), 2015 IEEE 9th International Conference on. IEEE (2015).
  12. [12] Talaie, Sharareh, Shoorehdeli and Mahdi Aliyari, “Nonlinear System Identification of Hammerstein Wiener Model Using AWPSO,” Intelligent Systems, Iranian Conference on (2014).
  13. [13] Yang, F., Chen, Z. and Wei, C., “Nonlinear System Modeling and Identification of Small Helicopter Based on Genetic Algorithm,” International Journal of Intelligent Computing &Cybernetics,Vol.6, No. 1, pp. 45 61 (2013). doi: 10.1108/17563781311301517
  14. [14] Barradas, F. M., Cunha, T. R., Lavrador, P. M. and Pedro, J. C., “Polynomials and LUTs in PA Behavioral Modeling: a Fair Theoretical Comparison,” IEEE Transactions on Microwave Theory and Techniques, Vol. 62, No. 12, pp. 32743285 (2014). doi: 10.1109/TMTT. 2014.2365188
  15. [15] Song, Q. K., Xu, M. M. and Liu, Y., “WaveletNetwork Controller Based on Improved Genetic Algorithm,” Measurement, Information and Control (ICMIC), 2013 International Conference on. IEEE, pp. 11111117 (2013).