A reduced complexity kalman-like algorithm for channel estimation and equalization

Yau Hee Kho, Desmond P. Taylor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The error rate performance of a previously developed reduced complexity channel estimator, known as the generalized least mean squares (GLMS) algorithm, is investigated in conjunction with a minimum-mean-square-error (MMSE) decision feedback equalizer (DFE). The channel estimator is based on the theory of polynomial prediction and Taylor series expansion of the underlying channel model in time domain. It is a simplification of the generalized recursive least squares (GRLS) estimator, achieved by replacing the online recursive computation of the 'intermediate' matrix by an offline pre-computed matrix. Similar to the GRLS estimator, it is able to operate in Rayleigh or Rician fading environment without reconfiguration of the state transition matrix to accommodate the non-random mean components. Simulation results show that it is able to offer a trade-off between reduced complexity channel estimation and good system performance.

Original languageEnglish
Title of host publicationIET 2nd International Conference on Wireless, Mobile and Multimedia Networks, ICWMMN 2008
Pages235-238
Number of pages4
Edition545 CP
DOIs
Publication statusPublished - Dec 1 2008
EventIET 2nd International Conference on Wireless, Mobile and Multimedia Networks, ICWMMN 2008 - Beijing, China
Duration: Oct 12 2008Oct 15 2008

Publication series

NameIET Conference Publications
Number545 CP

Other

OtherIET 2nd International Conference on Wireless, Mobile and Multimedia Networks, ICWMMN 2008
CountryChina
CityBeijing
Period10/12/0810/15/08

Keywords

  • Channel estimation
  • Equalization
  • Wireless communications

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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