An Application of Support Vector Regression on Narrow-Band Interference Suppression in Spread Spectrum Systems

• Published in: Advances in Natural Computation pp. 442 - 450
• Main Authors: Yang, Qing, Xie, Shengli
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Adaptive Filter; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Narrowband Interference; Structural Risk Minimization; Support Vector Regression; Suppression Technique; Computer simulation; Least squares method; Adaptive system; Vector support machine; Spread spectrum; Adaptive filter; Selection rule; Vector method; Narrow band
• ISBN: 9783540283256; 3540283250; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539117_65

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.