Approximate ML Decision-Feedback Block Equalizer for Doubly Selective Fading Channels

Lingyang Song, Rodrigo Caiado de Lamare, Are Hjorungnes, Alister G. Burr

Research output: Contribution to journalArticlepeer-review

Abstract

To effectively suppress intersymbol interference (ISI) at low complexity, in this paper, we propose an approximate maximum-likelihood decision-feedback block equalizer (A-ML-DFBE) for doubly selective (frequency- and time-selective) fading channels. The proposed equalizer design makes efficient use of the special time-domain representation of multipath channels through a matched filter, a sliding window, a Gaussian approximation, and a decision feedback. The A-ML-DFBE has the following features: 1) It achieves a performance that is close that to that of maximum-likelihood sequence estimation (MLSE) and significantly outperforms minimum mean square error (MMSE)-based detectors. 2) It has substantially lower complexity than conventional equalizers. 3) It easily realizes complexity and performance tradeoff by adjusting the length of the sliding window. 4) It has a simple and fixed-length feedback filter. The symbol error rate (SER) is derived to characterize the behavior of the A-ML-DFBE and can also be used to find the key parameters of the proposed equalizer. In addition, we further prove that the A-ML-DFBE obtains full multipath diversity.

Original languageEnglish
Pages (from-to)2314-2321
Number of pages8
JournalIEEE Transactions on Vehicular Technology
Volume58
Issue number5
DOIs
Publication statusPublished - Jun 2009

Keywords

  • Doubly selective fading channels
  • equalization
  • linear minimum mean square error (MMSE)
  • matched filter
  • maximum-likelihood sequence estimation (MLSE)
  • MMSE decision-feedback equalizer (MMSE-DFE)
  • INTERFERENCE
  • SYSTEMS
  • CODES

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