A reduced complexity channel estimation algorithm with equalization

Yau Hee Kho

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


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 a previously developed 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 publicationTENCON 2009 - 2009 IEEE Region 10 Conference
Publication statusPublished - Dec 1 2009
Event2009 IEEE Region 10 Conference, TENCON 2009 - Singapore, Singapore
Duration: Nov 23 2009Nov 26 2009

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON


Other2009 IEEE Region 10 Conference, TENCON 2009

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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