Blind channel estimation via combining autocorrelation and blind phase estimation


Baykal B.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, vol.51, no.6, pp.1125-1131, 2004 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 51 Issue: 6
  • Publication Date: 2004
  • Doi Number: 10.1109/tcsi.2004.829235
  • Journal Name: IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.1125-1131
  • Keywords: blind channel estimation, constant modulus algorithm, higher order statistics, matched filter receiver, HIGHER-ORDER STATISTICS, DECISION-FEEDBACK EQUALIZERS, SYSTEM-IDENTIFICATION, 2ND-ORDER STATISTICS, ADAPTIVE EQUALIZERS, CONSTANT MODULUS, EQUALIZATION, ALGORITHM

Abstract

Symbol spaced blind channel estimation methods are presented which can essentially use the results of any existing blind equalization method to provide a blind channel estimate of the channel. Blind equalizer's task is reduced to only phase equalization (or identification) as the channel autocorrelation is used to obtain the amplitude response of the channel. Hence, when coupled with simple algorithms such as the constant modulus algorithm (CMA) these methods at baud rate processing provide alternatives to blind channel estimation algorithms that use explicit higher order statistics (HOS) or second-order statistics (subspace) based fractionally-spaced/multichannel algorithms. The proposed methods use finite impulse response (FIR) filter linear receiver equalizer or matched filter receiver based infinite impulse response+FIR linear cascade equalizer configurations to obtain blind channel estimates. It is shown that the utilization of channel autocorrelation information together with blind phase identification of the CMA is very effective to obtain blind channel estimation. The idea of combining estimated channel autocorrelation with blind phase estimation can further be extended to improve the HOS based blind channel estimators in a way that the quality of estimates are improved.