The Kalman Filtering Blind Adaptive Multi-user Detector Based on Tracking Algorithm of Signal Subspace

Liqing Zhou,Henry Leung,Guobao Ru,Quansheng Zhao,Peng Xu,Da Xu

doi:10.3390/info6010003

Liqing Zhou, Henry Leung + Show 4 more

Open Access

https://doi.org/10.3390/info6010003

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Journal: Information	Publication Date: Jan 20, 2015
Citations: 22	License type: CC BY 4.0

Affiliation: Wuhan University, University of Calgary

Abstract

Multi-user detection is an effective method to reduce multiple access interference in code division multiple access (CDMA) systems. This paper discusses a signal subspace based blind adaptive multiuser detector and a Kalman filtering blind adaptive multiuser detector. Combining them together, a new Kalman filtering blind adaptive multiuser detector based on a tracking algorithm of the signal subspace is proposed. Analysis and simulation show that the proposed blind multiuser detector achieves better suppression of multiple access interference and has a higher convergence rate.

Highlights

IntroductionSpread spectrum codes are not completely orthogonal in a code division multiple access (CDMA)
Spread spectrum codes are not completely orthogonal in a code division multiple access (CDMA)mobile communication system, and non-zero cross correlation coefficients exists in spread spectrum sequences
Wang and Poor proposed a blind multi-user detector [8] based on subspace method; the idea is to use parameters of the signal subspace as the detector’s weight vectors and to apply a subspace tracking algorithm to get the information sequence of an anticipant user from the obtained detector’s weight vectors

Summary

Introduction

Spread spectrum codes are not completely orthogonal in a code division multiple access (CDMA). Wang and Poor proposed a blind multi-user detector [8] based on subspace method; the idea is to use parameters of the signal subspace as the detector’s weight vectors and to apply a subspace tracking algorithm to get the information sequence of an anticipant user from the obtained detector’s weight vectors. This method has the following advantages [9,10,11]: (1) low computational complexity; (2) robustness to background noise; (3) the multi-user detector’s weight vectors can adapt to a dynamic environment. Analyses and simulation results show that it has better multiple access interference suppression ability and a higher convergence rate

Signal and Channel Model

Kalman Filtering Blind Adaptive Multi-user Detector

The Blind Adaptive Multi-user Detector Based on Subspace Tracking Algorithm

Simulation Results and Analyses

Conclusions