Semi-blind algorithms for automatic classification of digital modulation schemes

  • Authors:

  • M.L. Dennis Wong;Asoke K. Nandi

  • Affiliations:

  • Information and Security Research Lab, Swinburne University of Technology (Sarawak Campus), Jalan Simpang Tiga, 93576 Kuching, Sarawak, Malaysia;Signal Processing and Communications Group, Department of Electrical Engineering and Electronics, The University of Liverpool, Brownlow Hill, Liverpool, L69 3GJ, UK

  • Venue:
  • Digital Signal Processing
  • Year:
  • 2008

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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.