Modulation Recognition based on Wavelet Transform and Fractal Theory

doi:10.23940/ijpe.19.03.p29.9981004

Abstract

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.

Key words: modulation recognition, wavelet analysis, fractal theory, feature extraction

Yanan Liu and Xinghao Guo. Modulation Recognition based on Wavelet Transform and Fractal Theory [J]. Int J Performability Eng, 2019, 15(3): 998-1004.

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