Empirical Bayes Deconvolution Based Modulation Discovery Under Additive Noise


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DÜLEK B.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, vol.67, no.7, pp.6668-6672, 2018 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 67 Issue: 7
  • Publication Date: 2018
  • Doi Number: 10.1109/tvt.2018.2800111
  • Journal Name: IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.6668-6672

Abstract

The problem of identifying digital amplitude-phase modulations under additive noise is addressed within the theory of empirical Bayes deconvolution. The presented methods employ parametric models in the observation and signal constellation spaces. The model parameters are estimated using the received samples and then substituted into the respective models to obtain the estimate for the signal constellation, from which the decoding of the received samples can be accomplished. The proposed framework can be used to construct a modulation dictionary for an unknown transmitter prior to employing any hypothesis testing-based classification algorithm.

The problem of identifying digital amplitude-phase modulations under additive noise is addressed within the theory of empirical Bayes deconvolution. The presented methods employ parametric models in the observation and signal constellation spaces. The model parameters are estimated using the received samples and then substituted into the respective models to obtain the estimate for the signal constellation, from which the decoding of the received samples can he accomplished. The proposed framework can he used to construct a modulation dictionary for an unknown transmitter prior to employing any hypothesis testing-based classification algorithm.