Superimposed training-based channel estimation and data detection for OFDM amplify-and-forward cooperative systems under high mobility

Lanlan He, Yik Chung Wu, Shaodan Ma, Tung Sang Ng, H. Vincent Poor

Research output: Contribution to journalArticle

27 Scopus citations

Abstract

In this paper, joint channel estimation and data detection in orthogonal frequency division multiplexing (OFDM) amplify-and-forward (AF) cooperative systems under high mobility is investigated. Unlike previous works on cooperative systems in which a number of subcarriers are solely occupied by pilots, partial data-dependent superimposed training (PDDST) is considered here, thus preserving the spectral efficiency. First, a closed-form channel estimator is developed based on the least squares (LS) method with Tikhonov regularization and a corresponding data detection algorithm is proposed using the linear minimum mean square error (LMMSE) criterion. In the derived channel estimator, the unknown data is treated as part of the noise and the resulting data detection may not meet the required performance. To address this issue, an iterative method based on the variational inference approach is derived to improve performance. Simulation results show that the data detection performance of the proposed iterative algorithm initialized by the LMMSE data detector is close to the ideal case with perfect channel state information.

Original languageEnglish (US)
Article number6025313
Pages (from-to)274-284
Number of pages11
JournalIEEE Transactions on Signal Processing
Volume60
Issue number1
DOIs
StatePublished - Jan 1 2012

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Amplify-and-forward
  • orthogonal frequency division multiplexing (OFDM)
  • time-varying channels

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