Blind channel equalization based on iterative weighted least-mean squared algorithm

Author

Xu, Dongxin ; Wu, Hsiao-Chun

Author_Institution

BBN Technol., A Verizon Co., Cambridge, MA, USA

Volume

6

fYear

2004

fDate

26-29 Sept. 2004

Firstpage

3833

Abstract

The expectation-maximization (EM) criterion has been widely applied for sparse observed data, such as time-varying wireless channel equalization. Previous EM techniques for joint channel estimation and symbol detection had computational complexity exponentially proportional to channel model order. In this paper, we derive an efficient iterative weighted least mean squared (IWLMS) algorithm, based on EM, for blind equalization. Our new IWLMS algorithm greatly outperforms the popular blind equalization method, based on the constant-modulas criteria, according to Monte Carlo experiments.

Keywords

Monte Carlo methods; blind equalisers; channel estimation; iterative methods; least mean squares methods; maximum likelihood estimation; IWLMS; Monte Carlo simulation; blind channel equalization; channel estimation; expectation-maximization criterion; iterative weighted LMS method; least-mean squared algorithm; maximum likelihood criterion; sparse observed data; symbol detection; time-varying wireless channel equalization; Blind equalizers; DH-HEMTs; Delay; Density functional theory; Distortion; Equations; Filtering; Gaussian noise; Iterative algorithms; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Vehicular Technology Conference, 2004. VTC2004-Fall. 2004 IEEE 60th

ISSN

1090-3038

Print_ISBN

0-7803-8521-7

Type

conf

DOI

10.1109/VETECF.2004.1404794

Filename

1404794