Asynchronous Linear Modulation Classification with Multiple Sensors via Generalized em Algorithm

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

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


In this paper, we consider the problem of automatic modulation classification with multiple sensors in the presence of unknown time offset, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) classification scheme based on a generalized expectation maximization (GEM) algorithm. GEM is capable of finding ML estimates numerically that are extremely hard to obtain otherwise. Assuming a good initialization technique is available for GEM, we show that the classification performance (in terms of the probability of error) can be greatly improved with multiple sensors compared to that with a single sensor, especially when the signal-to-noise ratio (SNR) is low. We further demonstrate the superior performance of our approach when simulated annealing (SA) with uniform as well as nonuniform grids is employed for initialization of GEM in low SNR regions. The proposed GEM based approach employs only a small number of samples (in the order of hundreds) at a given sensor node to perform both time and phase synchronization, signal power estimation, followed by modulation classification. We provide simulation results to show the efficiency and effectiveness of the proposed algorithm.

Original languageEnglish (US)
Article number7152981
Pages (from-to)6389-6400
Number of pages12
JournalIEEE Transactions on Wireless Communications
Issue number11
StatePublished - Nov 1 2015


  • Modulation classification
  • data fusion
  • generalized expectation maximization algorithm
  • hybrid maximum likelihood
  • multiple sensors

ASJC Scopus subject areas

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


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