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Modulation Recognition based on Wavelet Transform and Fractal Theory

Volume 15, Number 3, March 2019, pp. 998-1004
DOI: 10.23940/ijpe.19.03.p29.9981004

Yanan Liua and Xinghao Guob

aChina Research Institute of Radiowave Propagation, Qingdao, 266107, China
bHarbin Engineering University, Harbin, 150001, China

(Submitted on November 8, 2018; Revised on December 6, 2018; Accepted on January 2, 2019)

Abstract:

With the rapid development of communication technology, digital signal processing and other technologies, wireless communication environment is becoming more and more complex. Communication signals with different frequencies and modulated modes are usually scattered over a wide frequency band. In this paper, an improved algorithm based on wavelet transform and fractal theory is proposed. To improve the traditional fractal theory, wavelet transform is applied to the modulation signal, and then four fractal dimensions (Fractal box dimension, Petrosian fractal dimension, Katz fractal dimension and Sevcik fractal dimension) are used to extract the features. Through the simulation of the six modulation signals generated by Matlab, it can be seen that the recognition rate of the proposed method reaches 90% at the SNR of 2dB. Moreover, by comparing the method of this paper with the short-time Fourier transform and the fractional Fourier transform, we can find that the recognition rate of this method is 3% ~ 10% higher than the two comparison methods. It can be seen that the proposed method can effectively identify different signals in the case of low SNR.

 

References: 18

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        12. A. Abdelmutalab, K. Assaleh, and M. El-Tarhuni, “Automatic Modulation Classification based on High Order Cumulants and Hierarchical Polynomial Classifiers,” Physical Communication, Vol. 21, pp. 10-18, 2016
        13. A. Ebrahimzadeh and R. Ghazalian, “Blind Digital Modulation Classification in Software Radio using the Optimized Classifier and Feature Subset Selection,” Engineering Applications of Artificial Intelligence, Vol. 24, No. 1, pp. 50-59, 2011
        14. A. Ebrahimzadeh, H. Azimi, and S. A. Mirbozorgi, “Digital Communication Signals Identification using an Efficient Recognizer,” Measurement, Vol. 44, No. 8, pp. 1475-1481, 2011
        15. K. Hassan, I. Dayoub, W. Hamouda, et al., “Automatic Modulation Recognition using Wavelet Transform and Neural Networks in Wireless Systems,” EURASIP Journal on Advances in Signal Processing, Vol. 2010, No. 1, pp. 532898, 2010
        16. K. Hassan, I. Dayoub, W. Hamouda, et al., “Automatic Modulation Recognition using Wavelet Transform and Neural Network,” in Proceedings of International Conference on Intelligent Transport Systems Telecommunications, IEEE, 2010
        17. K. Maliatsos, S. Vassaki, and P. Constantinou, “Interclass and Intraclass Modulation Recognition using the Wavelet Transform,” in Proceedings of IEEE International Symposium on Personal, IEEE, 2007
        18. Y. Feng, X. Zhang, Q. Bo, et al., “Research on Modulation Recognition Method of MSK Signals based on Wavelet Transform,” in Proceedings of the Seventh International Symposium on Computational Intelligence & Design, 2014

         

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        1.        H. Han, J. C. Li, and X. Chen, The Individual Identification Method of Wireless Device based on a Robust Dimensionality Reduction Model of Hybrid Feature Information, Mobile Networks and Applications, Vol. 23, No. 4, pp. 709-716, August 2018

        2.        J. Yang, H. Liu, X. Y. Bu, et al., Modulation Recognition for Communication Signals: Principles and Algorithms,” Beijing Posts and Telecom Press, 2014

        3.        J. C. Li and Y. L. Ying, A Method to Improve the Robustness of Gas Turbine Gas-Path Fault Diagnosis Against Sensor Faults, IEEE Transactions on Reliability, Vol. 67, No. 1, pp. 3-12, March 2018

        4.        Y. L. Ying, J. C. Li, J. Li, and Z. M. Chen, Study on Rolling Bearing On-Line Reliability Analysis based on Vibration Information Processing,” Computers and Electrical Engineering, Vol. 69, pp. 842-851, 2018

        5.        J. C. Li, Y. P. Cao, Y. L. Ying, and S. Y. Li, A Rolling Element Bearing Fault Diagnosis Approach based on Multifractal Theory and Gray Relation Theory,” PLOS ONE, Vol. 11, No. 12, 2016

        6.        X. Y. Gu, Research on Modulation Recognition Algorithm of Digital Communication Signal based on Wavelet Denoising,” Applied Mechanics & Materials, pp. 608-609:459-467, 2014

        7.        P. H. Li, H. X. Zhanu, and X. Y. Wand, Modulation Recognition of Communication Signals based on High Order Cumulants and Support Vector Machine,” The Journal of China Universities of Posts and Telecommunications, Vol. 19, No. 11, pp. 61-65, 2012

        8.        Z. Y. Ma, F. L. Han, and Z. D. Xie, Modulation Recognition Technology of Satellite Communication Signal System,” Acts Aeronautics et Astronautics Sinica, Vol. 35, No. 12, pp. 3403-3414, 2014

        9.        Y. Zhao, Y. T. Xu, H. Jiang, et al., Recognition of Digital Modulation Signals based on High-Order Cumulants,” in Proceedings of International Conference on Wireless Communications & Signal Processing, IEEE, 2015

        10.     D. C. Chang and P. K. Shih, Cumulants-based Modulation Classification Technique in Multipath Fading Channels,” IET Communications, Vol. 9, No. 6, pp. 828-835, 2015

        11.     H. D. Liu, H. X. Zhang, and H. E. Peng-Fei, Study on Hybrid Pattern Recognition Algorithm for Modulated Signals,” Journal of China Universities of Posts & Telecommunications, Vol. 21, No. 14, pp. 106-109, 2014

        12.     A. Abdelmutalab, K. Assaleh, and M. El-Tarhuni, Automatic Modulation Classification based on High Order Cumulants and Hierarchical Polynomial Classifiers,” Physical Communication, Vol. 21, pp. 10-18, 2016

        13.     A. Ebrahimzadeh and R. Ghazalian, Blind Digital Modulation Classification in Software Radio using the Optimized Classifier and Feature Subset Selection,” Engineering Applications of Artificial Intelligence, Vol. 24, No. 1, pp. 50-59, 2011

        14.     A. Ebrahimzadeh, H. Azimi, and S. A. Mirbozorgi, Digital Communication Signals Identification using an Efficient Recognizer,” Measurement, Vol. 44, No. 8, pp. 1475-1481, 2011

        15.     K. Hassan, I. Dayoub, W. Hamouda, et al., Automatic Modulation Recognition using Wavelet Transform and Neural Networks in Wireless Systems,” EURASIP Journal on Advances in Signal Processing, Vol. 2010, No. 1, pp. 532898, 2010

        16.     K. Hassan, I. Dayoub, W. Hamouda, et al., Automatic Modulation Recognition using Wavelet Transform and Neural Network,” in Proceedings of International Conference on Intelligent Transport Systems Telecommunications, IEEE, 2010

        17.     K. Maliatsos, S. Vassaki, and P. Constantinou, Interclass and Intraclass Modulation Recognition using the Wavelet Transform,” in Proceedings of IEEE International Symposium on Personal, IEEE, 2007

        Y. Feng, X. Zhang, Q. Bo, et al., Research on Modulation Recognition Method of MSK Signals based on Wavelet Transform,” in Proceedings of the Seventh International Symposium on Computational Intelligence & Design, 2014
         
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