Modulation Classification for MIMO-OFDM Signals via Approximate Bayesian Inference

Yu Liu, Osvaldo Simeone, Alexander M. Haimovich, Wei Su

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


The problem of modulation classification for a multiple-antenna (multiple-input multiple-output (MIMO)) system employing orthogonal frequency-division multiplexing (OFDM) is investigated under the assumption of unknown frequency-selective fading channels and signal-to-noise ratio (SNR). The classification problem is formulated as a Bayesian inference task, and solutions are proposed based on Gibbs sampling and mean field variational inference. The proposed methods rely on a selection of the prior distributions that adopts a latent Dirichlet model for the modulation type and on the Bayesian network (BN) formalism. The Gibbs sampling method converges to the optimal Bayesian solution, and using numerical results, its accuracy is seen to improve for small sample sizes when switching to the mean field variational inference technique after a number of iterations. The speed of convergence is shown to improve via annealing and random restarts. While most of the literature on modulation classification assumes that the channels are flat fading, that the number of receive antennas is no less than that of transmit antennas, and that a large number of observed data symbols are available, the proposed methods perform well under more general conditions. Finally, the proposed Bayesian methods are demonstrated to improve over existing non-Bayesian approaches based on independent component analysis (ICA) and on prior Bayesian methods based on the 'superconstellation' method.

Original languageEnglish (US)
Article number7425264
Pages (from-to)268-281
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Issue number1
StatePublished - Jan 2017

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Automotive Engineering


  • Bayesian inference
  • Gibbs sampling
  • latent Dirichlet model
  • mean field variational inference
  • modulation classification
  • multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM)


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