Wavelet-Based Sequential Monte Carlo Blind Receivers in Fading Channels with Unknown Channel Statistics

Dong Guo, Xiaodong Wang, Rong Chen

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Recently, an adaptive Bayesian receiver for blind detection in flat-fading channels was developed by the present authors, based on the sequential Monte Carlo methodology. That work is built on a parametric modeling of the fading process in the form of a state-space model and assumes the knowledge of the second-order statistics of the fading channel. In this paper, we develop a nonparametric approach to the problem of blind detection in fading channels, without assuming any knowledge of the channel statistics. The basic idea is to decompose the fading process using a wavelet basis and to use the sequential Monte Carlo technique to track both the wavelet coefficients and the transmitted symbols. A novel resampling-based wavelet shrinkage technique is proposed to dynamically choose the number of wavelet coefficients to best fit the fading process. Under such a framework, blind detectors for both flat-fading channels and frequency-selective fading channels are developed. Simulation results are provided to demonstrate the excellent performance of the proposed blind adaptive receivers.

Original languageEnglish (US)
Pages (from-to)227-239
Number of pages13
JournalIEEE Transactions on Signal Processing
Volume52
Issue number1
DOIs
StatePublished - Jan 2004
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Adaptive shrinkage
  • Fading channel
  • Resampling
  • Sequential Monte Carlo
  • Wavelet

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