Robust multiuser detection method based on neural-net preprocessing in impulsive noise environment

Authors:
Ying Guo;Tianshuang Qiu
Affiliations:
School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 3
Cited 0

Signal processing with alpha-stable distributions and applications

Signal processing with alpha-stable distributions and applications
Multiuser Detection

Multiuser Detection
Non-Gaussian noise models in signal processing for telecommunications: new methods an results for class A and class B noise models

IEEE Transactions on Information Theory

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Abstract

This paper models the ambient noise as α stable distribution and proposes a novel robust multiuser detection (MUD) method that involves an adaptive nonlinear preprocessor based on multilayer perceptron neural-network whose action is to suppress the negative effect of impulsive noises on the followed decorrelating decision-feedback (DDF) multiuser detector. Simulation results indicate the proposed new method is robust and offers performance enhancement over traditional technology in impulsive noises.