Modulation classification for MIMO-OFDM signals via Gibbs sampling

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

2 Scopus citations

Abstract

The problem of modulation classification for a multiple-antenna (MIMO) system employing orthogonal frequency division multiplexing (OFDM) is investigated under the assumptions of unknown frequency-selective fading channels and signal-to-noise ratio (SNR). The classification problem is formulated as a Bayesian inference task and a solution is proposed based on a selection of the prior distributions that adopts a latent Dirichlet model for the modulation type and on the Bayesian network formalism. The proposed Gibbs sampling method converges to the optimal Bayesian solution and the speed of convergence is shown to improve via annealing and random restarts. While most of the existing modulation classification techniques works under the assumptions that the channels are flat fading and that a large amount of observed data symbols is available, the proposed approach performs well under more general conditions. Finally, the proposed Bayesian method is demonstrated to improve over existing non-Bayesian approaches based on independent component analysis.

Original languageEnglish (US)
Title of host publication2015 49th Annual Conference on Information Sciences and Systems, CISS 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479984282
DOIs
StatePublished - Apr 15 2015
Event2015 49th Annual Conference on Information Sciences and Systems, CISS 2015 - Baltimore, United States
Duration: Mar 18 2015Mar 20 2015

Publication series

Name2015 49th Annual Conference on Information Sciences and Systems, CISS 2015

Other

Other2015 49th Annual Conference on Information Sciences and Systems, CISS 2015
CountryUnited States
CityBaltimore
Period3/18/153/20/15

All Science Journal Classification (ASJC) codes

  • Information Systems

Keywords

  • Gibbs sampling
  • MIMO-OFDM
  • Modulation classification

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