Hybrid maximum likelihood modulation classification using multiple radios

Onur Ozdemir, Ruoyu Li, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


In this paper, we focus on amplitude-phase modulations and propose a modulation classification framework based on centralized data fusion using multiple radios and the hybrid maximum likelihood (ML) approach. In order to alleviate the computational complexity associated with ML estimation, we adopt the Expectation Maximization (EM) algorithm. Due to SNR diversity, the proposed multi-radio framework provides robustness to channel SNR. Numerical results show the superiority of the proposed approach with respect to single radio approaches as well as to modulation classifiers using moments based estimators.

Original languageEnglish (US)
Article number6584527
Pages (from-to)1889-1892
Number of pages4
JournalIEEE Communications Letters
Issue number10
StatePublished - 2013


  • EM algorithm
  • ML estimation
  • Modulation classification
  • data fusion

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering


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