Nonparametric multiuser detection in non-Gaussian channels

Existing multiuser detection techniques in wireless systems are based on the assumption that some information on the parameters of the probability density function (pdf) of ambient noise is available. Such information may not be available in all cases, particularly for non-Gaussian and impulsive noi...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on signal processing 2006-01, Vol.54 (1), p.23-33
Main Authors: Seyfe, B., Sharafat, A.R.
Format: Article
Language:English
Subjects:
Analytical models
Applied sciences
Computational complexity
Computational modeling
Detection, estimation, filtering, equalization, prediction
Detectors
Exact sciences and technology
Information analysis
Information, signal and communications theory
Mathematical analysis
Mathematical models
Multiuser detection
Noise
Noise robustness
Non-Gaussian
Nonparametric multiuser detection
Norms
Performance analysis
Probability density function
Probability density functions
pseudonorm
Regression
sign detector
Signal and communications theory
Signal, noise
Studies
Telecommunications and information theory
Wilcoxon detector
Working environment noise
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Existing multiuser detection techniques in wireless systems are based on the assumption that some information on the parameters of the probability density function (pdf) of ambient noise is available. Such information may not be available in all cases, particularly for non-Gaussian and impulsive noises, or may change depending on circumstances. In this paper, we present a technique for multiuser detection that does not require any a priori knowledge about the noise parameters. This method is based on using pseudo norms for linear nonparametric regression. Analytical and simulation results show that the proposed method offers an improved, or at least comparable, performance over existing robust techniques in the absence of any information on the nature of noise in the environment. The increased computational complexity is marginal compared to existing parametric detectors. In addition, the proposed nonparametric detector is portable in the sense that it does not need to be tuned for different noise models without any considerable degradation of performance. We also show that in non-Gaussian noise, the performance of blind adaptive nonparametric multiuser detectors is better than that of robust multiuser detectors.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2005.861102