Semi-blind algorithms for automatic classification of digital modulation schemes

M. L. Dennis Wong, Asoke K. Nandi

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)

Abstract

The problem of automatic classification of digital communication modulation schemes is considered in this work. Firstly, the maximum likelihood (ML) classifier for classifying phase-amplitude modulated schemes in coherent environment is presented. It is well known that the ML classifier requires the knowledge of the signal-to-noise ratio (SNR) and has a higher computational complexity. To relax the first requirement, we introduce a novel idea to estimate the SNR and this gives rise to a novel estimated ML (EsML) classifier. After which, in an attempt to reduce the computational complexity of the EML and EsML classifiers, we propose a simplified minimum distance (MD) classifier. The performance of these classifiers are compared against each other's under the ideal channel condition as well as under a channel condition with an unknown carrier phase offset. In the second part of the paper, we adapt a closed form blind source separation (BSS) algorithm for rectifying the carrier phase offset prior to the actual classification procedures.

Original languageEnglish
Pages (from-to)209-227
Number of pages19
JournalDigital Signal Processing
Volume18
Issue number2
DOIs
Publication statusPublished - Mar 2008

Keywords

  • Independent component analysis
  • Maximum likelihood methods
  • Modulation classification
  • Phase offset removal

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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