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Doubly-selective MIMO-OFDM channel identification using superimposed training

  • Weixiao Meng*
  • , Junyi Zhao
  • , Shilou Jia
  • *Corresponding author for this work
  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In order to estimate doubly-selective MIMO-OFDM channel meanwhile improve bandwidth efficiency, a superimposed training (ST) method is considered. The time-varying channel is assumed to be approximated by a complex exponential basis expansion model (CE-BEM). A periodic (non-random) training sequence is arithmetically superimposed at a low power to the information sequence at the transmitter, channel parameters could be obtained without loss of bandwidth. The unknown information sequence can be interference to the ST channel estimation method, in this paper an iterative ST (IST) channel estimation method is presented to improve channel estimation performance exploiting equalized information symbols. From the result of computer simulations, we show that the proposed method can achieve good MSE and BER performance.

Original languageEnglish
Title of host publication2009 Canadian Conference on Electrical and Computer Engineering, CCECE '09
Pages762-765
Number of pages4
DOIs
StatePublished - 2009
Event2009 Canadian Conference on Electrical and Computer Engineering, CCECE '09 - St. Johns, NL, Canada
Duration: 3 May 20096 May 2009

Publication series

NameCanadian Conference on Electrical and Computer Engineering
ISSN (Print)0840-7789

Conference

Conference2009 Canadian Conference on Electrical and Computer Engineering, CCECE '09
Country/TerritoryCanada
CitySt. Johns, NL
Period3/05/096/05/09

Keywords

  • Channel estimation
  • Doubly-selective channel
  • Iterative process
  • Superimposed training

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