Abstract
Based on fractal theory, the note presents a novel method of modulation signals classification that adopts box dimension and information dimension extracted from received signals as features of classification. These features contain the characteristics of magnitude, frequency and phase of signals, and collect discriminatory information among various modulation modes. They are effective features in classification sense, and are insensitive to noises interfering. The theoretical analysis also proves the above conclusion. The classifier design is very simple based on such features. The simulation results show that the performances of signal classification are superior.
Similar content being viewed by others
References
Assaleh, K., Farrall, K., Mammone, R. J., A new method of modulation classification for digitally modulated signals, Proc. MILCOM’92, San Diego, CA: IEEE Operations Center, Piscataway, NJ 08855, 1992, 0712–0716.
Ho, K. C., Prokopiw, W., Chan. Y. T., Modulation identification by the wavelet transform, Proc. MILCOM’95, San Diego, CA: IEEE Operations Center, Piscataway, NJ 08855, 1995, 886–890.
Dominguez, L. V., Paez Borrallo, J. M., A general approach to the automatic classification of radiocommunication signals, Signal Processing, 1991, 22: 239–250.
Nandi, A. K., Azzouz, E. E., Modulation recognition using artificial neural networks, Signal Processing, 1997, 56: 165–175.
Xie Heping, Xue Xiuqian, The Mathematical Foundation and Method of Fractal Application (in Chinese), Beijing: Science Press, 1998.
Lu Mingquan, Xiao Xianci, Li Lemin, A new digital modulation recognition method using features extracted from GAR model parameters, Journal of Electronics, 1999, 16(3): 244–250.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Lü, T., Guo, S. & Xiao, X. Study on fractal features of modulation signals. Sci China Ser F 44, 152–158 (2001). https://doi.org/10.1007/BF02713973
Received:
Issue Date:
DOI: https://doi.org/10.1007/BF02713973