Asynchronous hybrid maximum likelihood classification of linear modulations

Onur Ozdemir, Pramod K. Varshney, Wei Su

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


In this paper, we consider the problem of linear modulation classification in the presence of unknown time offset, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) approach based on a Generalized Expectation Maximization (GEM) algorithm [1]. Our approach is applicable to all QAM and PSK modulations, and it does not require any assumptions on the received signal-to-noise ratio (SNR). The GEM algorithm provides a tractable procedure to obtain maximum likelihood (ML) estimates which are extremely hard to obtain otherwise. Moreover, our approach employs only a small number of samples (in the order of hundreds) to perform both time and phase synchronization, signal power estimation, followed by modulation classification. The proposed approach also enables maximum a posteriori (MAP) decoding of the unknown constellation symbol sequence as a by-product of the GEM algorithm. We provide simulation results that show that the proposed approach provides excellent classification performance.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Communications Conference, GLOBECOM 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781479913534
StatePublished - Jan 1 2013
Event2013 IEEE Global Communications Conference, GLOBECOM 2013 - Atlanta, GA, United States
Duration: Dec 9 2013Dec 13 2013

Publication series

NameGLOBECOM - IEEE Global Telecommunications Conference


Other2013 IEEE Global Communications Conference, GLOBECOM 2013
Country/TerritoryUnited States
CityAtlanta, GA


  • Modulation classification
  • generalized expectation maximization
  • hybrid maximum likelihood
  • time offset

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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