Abstract
The conventional approaches to suppress the narrow-band interference of spread spectrum systems mostly use the adaptive LMS filter to predict the narrow-band interference and subtract the predicted interfering signal from the polluted received signal before de-spreading. However, since these approaches take no account of complexity control and have no guarantee of global minimum, they often suffer from unsteady performance. In this paper, a novel approach to narrow-band interference suppression is proposed, in which ε – support vector regression method is used to predict the narrow-band interference instead of adaptive LMS filter. With the help of practical parameter selection rules, it is not only effective but also easy to handle. Computer simulations show that it outperforms the conventional approaches in most cases and thus is a desirable choice for narrow-band interference suppression in spread spectrum systems.
This work was supported by the National Natural Science Foundation of China (Grant 60274006), the Natural Science Key Fund of Guangdong Province, P.R.China (Grant 020826), the National Natural Science Foundation of China for Excellent Youth (Grant 60325310), and the Trans-Century Training Programme Foundation for the Talents by the State Education Ministry.
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References
Haykin, S.: Adaptive Filter Theory, 3rd edn. Prentice-Hall, Inc., Englewood Cliffs (1996)
Smola, A.: Regression estimation with support vector machines (December 1996)
Kwok, J.T., Tsang, I.W.: Linear dependency between ε and the input noise in ε-support vector regression. IEEE Transactions on Neural Networks 14(3), 544–553 (2003)
Cherkassky, V., Ma, Y.: Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 7, 113–126 (2004)
Vijayan, R., Poor, H.V.: Nonlinear techniques for interference suppression in spread-spectrum systems. IEEE Transactions on Communications 38, 1060–1065 (1990)
Hua, Y., Gong, Y., Li, H., Zhou, Z.: The realization of narrow-band interferences suppression in derect sequence spread spectrum signal using frequency-domain algorithm (2002)
Rusch, L.A., Poor, H.V.: Narrowband interference suppression in CDMA spread spectrum communications. IEEE Transactions on Communications 42, 1969–1979 (1994)
Ferrara, E.R.: Fast implementation of LMS adaptive filters. IEEE Transactions on Speech and Signal Processing ASSP-28, 474–475 (1980)
Saulnier, G.J.: Suppression of narrowband jammers in a spread-spectrum receiver using transform-domain adaptive filter. IEEE Journal on Selected Areas in Communications 10, 742–749 (1992)
Milstein, L.B.: Interference rejection techniques in spread spectrum communications. Proceedings of the IEEE 76, 657–671 (1988)
Haykin, S.: Adaptive Filter Theory, 3rd edn. Prentice-Hall, Inc., Englewood Cliffs (1996)
Smola, A., Scholkopf, B.: A tutorial on support vector regression (September 2003)
Vapnik, V.N., Golowich, S.E., Smola, A.J.: Support vector method for function approximation, regression estimation and signal processing (1996)
Muller, K.R., Smola, A., Ratsch, G., Scholkopf, B., Kohlmorgen, J., Vapnik, V.: Predicting time series with support vector machines. In: Proc. ICANN (1997)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, Inc., Englewood Cliffs (1999)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
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Yang, Q., Xie, S. (2005). An Application of Support Vector Regression on Narrow-Band Interference Suppression in Spread Spectrum Systems. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_65
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DOI: https://doi.org/10.1007/11539117_65
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