Distributed maximum likelihood classification of linear modulations over nonidentical flat block-fading gaussian channels

Berkan Dulek, Onur Ozdemir, Pramod K. Varshney, Wei Su

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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 blockfading 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.

Original languageEnglish (US)
Article number6902810
Pages (from-to)724-737
Number of pages14
JournalIEEE Transactions on Wireless Communications
Issue number2
StatePublished - Feb 2015


  • Distributed modulation classification
  • fading channels
  • maximum likelihood
  • wireless sensor networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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