Abstract
In order to solve the problem that modulation recognition of MFSK signals in alpha-stable distribution noise, a novel algorithm using multifractal spectrum is proposed. Multifractal spectrum characteristics of signals and noise are discussed firstly. Then algorithm extracts the difference between maximum and minimum values of spectrum as classification feature. Finally, algorithm employs threshold decision method to achieve modulation recognition of 2FSK, 4FSK and 8FSK signals. Numerical results show that algorithm has good performance in both alpha-stable distribution noise and Gaussian noise, and it is less affected by characteristic exponent of noise and data points.
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Dobre, O. A., Abdi, A., Bar-Ness, Y., & Su, W. (2010). Cyclostationarity-based modulation classification of linear digital modulations in flat fading channels. Journal of Wireless Personal Communications, 54(4), 699–717.
Rajan, A., & Tepedelenlioğlu, C. (2010). Diversity combining over Rayleigh fading channels with symmetric alpha-stable noise. Journal of IEEE Transactions on Wireless Communication, 9(9), 2968–2976.
Wang, J., Kuruoglu, E. E., & Zhou, T. (2011). Alpha-stable channel capacity. IEEE Communications Letters, 15(10), 1107–1109.
Park, J., Shevlyakov, G., & Kim, K. (2011). Maximin distributed detection in the presence of impulsive alpha-stable noise. IEEE Transactions on Wireless Communications, 10(6), 1687–1691.
Khalil, H. K., Clavier, L., & Septier, F. (2011). Performance of an optimal receiver in the presence of alpha-stable and Gaussian noises. In Proceedings of IEEE statistical signal processing workshop: June 28–30, 2011 (pp. 573–576). Nice, France: IEEE Press.
Li, J., Jing, L. U., & Zhao, J. (2010). A robust constant modulus algorithm in alpha-stable noise environments. In Proceedings of IEEE 10th international conference on signal processing: October 24–28, 2010 (pp. 1589–1592). Beijing, China: IEEE Press.
Prakasam, P., & Madheswaran, M. (2009). Intelligent decision making system for digital modulation scheme classification in software radio using wavelet transform and higher order statistical moments. Wireless Personal Communications, 50(4), 509–528.
Zhou, X., Wu, Y., & Yang, B. (2010). Signal classification method based on support vector machine and high-order cumulants. Wireless Sensor Network, 2(1), 48–52.
Youyong, L., Guolong, L., Xiaoka, X., et al. (2008). The methods of recognition for common used M-ary digital modulations. In Proceedings of the 4th international conference on wireless communications, networking and mobile: October 12–14, 2008 (pp. 1–4). Dalian, China: IEEE Press.
Prakasam, P., & Madheswaran, M. (2008). M-ary shift keying modulation scheme identification algorithm using wavelet transfonn and higher order statistical moment. Journal of Applied Sciences, 8(1), 112–119.
Yiqiong, X. U., Ge, L., & Wang, B. (2009). Digital modulation recognition method based on tree-structured neural networks. In Proceedings of the 2009 international conference on communication software and networks: February 27–28, 2009 (pp. 708–712). Macau, China: IEEE Press.
Zhang, D. T., & Luo, F. (2011). A new detecting method for weak targets in sea clutter based on multifractal properties. In Proceedings of 2011 IEEE CIE international conference on Radar October 24–27, 2011 (pp. 446–449). Chengdu, China: IEEE Press.
Zhou, Y., Wang, J., & Zhang, X. (2010). Research on speaker recognition based on multifractal spectrum feature. In Proceedings of international conference on computer modeling and simulation: January 22–24, 2010 (pp. 463–466). Sanya, China: IEEE Press.
Guan, J., Liu, N., Zhang, J. & Song, J. (2010). Multifractal correlation characteristic for radar detecting low-observable target in sea clutter. Signal Processing, 90(2), 523–535.
An, J., Tian, B., Yi, K., et al. (2011). Blind identification algorithm of OFDM signals based on multifractal features. Journal of South China University of Technology (Natural Science), 39(7), 50–55.
Zhang, Y., Zhao, J., Guo, Y., et al. (2010). A constant modulus algorithm for blind equalization in \(\alpha \)-stable noise. Applied Acoustics, 71(7), 653–660.
Zha, D., & Qiu, T. (2007). Direction finding in non-Gaussian impulsive noise environments. Digital Signal Processing, 17(2), 451–465.
Tsihrintzis, G. A., & Nikias, C. L. (1996). Fast estimation of the parameters of alpha-stable impulsive interference. IEEE Transactions on Signal Processing, 44(6), 1492–1503.
Bates, S., & Mclaughlin, S. (2000). The estimation of stable distribution parameters from teletraffic data. IEEE Transactions on Signal Processing, 48(3), 865–870.
Sun, Y., & Qiu, T. (2010). The SCOT based, weighted adaptive time delay estimation algorithm, & on minimum dispersion criterion. In Proceedings of international conference on intelligent control and information processing: August 13–15, 2010 (pp. 35–38). Dalian, China: IEEE Press.
Lopes, R., & Betrouni, N. (2009). Fractal and multifractal analysis: A review. Medical Image Analysis, 13(4), 634–649.
Chen, S. P., & He, L. (2010). Multifractal spectrum analysis of nonlinear dynamical mechanisms in China’s agricultural futures markets. Physica A: Statistical Mechanics and its Applications, 389(7), 1434–1444.
De Souza, J., & Pires Rostirolla, S. (2011). A fast MATLAB program to estimate the multifractal spectrum of multidimensional data: Application to fractures. Computers and Geosciences, 37(2), 241–249.
Le, K. N., & Dabke, K. P. (2010). BER of OFDM with diversity and pulse shaping in Rayleigh fading environments. Digital Signal Processing, 20(6), 1687–1696.
Le, K. N. (2011). A mathematical approach to edge detection in hyperbolic-distributed and Gaussian-distributedpixel-intensity images using hyperbolic and Gaussian masks. Digital Signal Processing, 21(1), 162–181.
Le, K. N., Dabke, K. P., & Egan, G. K. (2006). On mathematical derivations of auto-term functions and signalto-noise ratios of Choi-Williams, first- and nth-order hyperbolic kernels. Digital Signal Processing, 16(1), 84–104.
Le, K. N. (2008). Bounds on inter-carrier interference power of OFDM in a Gaussian scattering channel. Wireless Personal Communications, 47(3), 355–362.
Le, K. N. (2008). Inter-carrier interference power of OFDM in a uniform scattering channel. Computer Communications, 31(17), 3883–4230.
Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant No. 61077079; the Ph.D. Programs Foundation of Ministry of Education of China under Grant No. 20102304110013 and the Fundamental Research Funds for the Central Universities under Grant No. HEUCF1208.
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Zhao, C., Yang, W. Modulation Recognition of MFSK Signals Based on Multifractal Spectrum. Wireless Pers Commun 72, 1903–1914 (2013). https://doi.org/10.1007/s11277-013-1112-1
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DOI: https://doi.org/10.1007/s11277-013-1112-1