Multiuser Detection in Heavy Tailed Noise

Abstract

Zoubir, A. M., and Brcich, R. F., Multiuser Detection in Heavy Tailed Noise, Digital Signal Processing 12 (2002) 262–273 We consider the problem of multiuser detection in impulsive noise channels. Multiuser detection methods have been shown to effectively combat multiple access interference in Gaussian noise, but are highly vulnerable to impulsive noise common in urban and indoor areas. Many multiuser detectors proposed for impulsive noise are based on a specific parametric noise model. While such a detector will perform well near the chosen model, performance is uncertain under larger deviations from the model. Robust detectors seek to minimize this loss, though they still rely on a static, albeit broader, model. We propose a nonparametric detector which makes minimal a priori assumptions on the noise model, requiring only a symmetric density. The detector is based on a nonparametric estimate of the noise density, obtained from the observations without the need for training data. Simulations show the nonparametric detector offers improved performance over existing methods when the noise is highly impulsive.