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

  • Qing Yang
  • Shengli Xie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3611)

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.

Keywords

Support Vector Regression Adaptive Filter Structural Risk Minimization Suppression Technique Narrowband Interference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Qing Yang
    • 1
  • Shengli Xie
    • 1
  1. 1.Electrical Engineering DepartmentSouth China University of TechnologyGuangzhouP.R. China

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