An Improved Particle Smoother for Blind Equalization in Time-Varying MIMO Channels

Author

Yihua Yu

Author_Institution

Sch. of Sci., Beijing Univ. of Posts & Telecommun., Beijing, China

Volume

19

Issue

6

fYear

2015

fDate

Jun-15

Firstpage

929

Lastpage

932

Abstract

An improved fixed-lag particle smoother in the mixture Kalman filter (MKF) framework is developed for the blind equalization in time-varying multiple-input multiple-output (MIMO) channels. The improved particle smoother utilizes the uniform proposal distribution (PD) and the optimal resampling to generate particles. Compared to the particle smoother with the posterior PD, the improved particle smoother can provide more accurate estimation results, while both smoothers have same computational complexity. Simulation results are provided to illustrate the performance of the methods.

Keywords

Kalman filters; MIMO communication; blind equalisers; particle filtering (numerical methods); time-varying channels; MKF framework; blind equalization; computational complexity; fixed-lag particle smoother improvement; mixture Kalman filter framework; optimal resampling; posterior PD; time-varying MIMO channels; time-varying multiple-input multiple-output channels; uniform proposal distribution; Bit error rate; Blind equalizers; Kalman filters; MIMO; Monte Carlo methods; Receiving antennas; Signal to noise ratio; Equalization; fixed-lag smoother; mixture Kalman filter; multiple-input multiple-output; multiple-input multiple-output (MIMO); particle filter;

fLanguage

English

Journal_Title

Communications Letters, IEEE

Publisher

ieee

ISSN

1089-7798

Type

jour

DOI

10.1109/LCOMM.2015.2425890

Filename

7101233