Distributed Maximum Likelihood Classification of Linear Modulations Over Nonidentical Flat Block-Fading Gaussian Channels

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DÜLEK B., Özdemir O., Varshney P. K., Su W.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, vol.14, no.2, pp.724-737, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 2
  • Publication Date: 2015
  • Doi Number: 10.1109/twc.2014.2359019
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.724-737
  • Keywords: Distributed modulation classification, fading channels, maximum likelihood, wireless sensor networks, EM ALGORITHM, CONSENSUS
  • Hacettepe University Affiliated: Yes


In this paper, we consider distributed maximum likelihood (ML) classification of digital amplitude-phase modulated signals using multiple sensors that observe the same sequence of unknown symbol transmissions over nonidentical flat block-fading Gaussian noise channels. A variant of the expectation-maximization (EM) algorithm is employed to obtain the ML estimates of the unknown channel parameters and compute the global log-likelihood of the observations received by all the sensors in a distributed manner by means of an average consensus filter. This procedure is repeated for all candidate modulation formats in the reference library, and a classification decision, which is available at any of the sensors in the network, is declared in favor of the modulation with the highest log-likelihood score. The proposed scheme improves the classification accuracy by exploiting the signal-to-noise ratio (SNR) diversity in the network while restricting the communication to a small neighborhood of each sensor. Numerical examples show that the proposed distributed EM-based classifier can achieve the same classification performance as that of a centralized classifier, which has all the sensor measurements, for a wide range of SNR values.