Modulation classification via gibbs sampling based on a latent dirichlet bayesian network

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels. The method is based on Gibbs sampling as applied to a latent Dirichlet Bayesian network (BN). The use of the proposed latent Dirichlet BN provides a systematic solution to the convergence problem encountered by the conventional Gibbs sampling approach for modulation classification. The method generalizes, and is shown to improve upon, the state of the art.

Original languageEnglish (US)
Article number6822569
Pages (from-to)1135-1139
Number of pages5
JournalIEEE Signal Processing Letters
Volume21
Issue number9
DOIs
StatePublished - Sep 2014

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

Keywords

  • Bayesian network
  • Gibbs sampling
  • latent dirichlet
  • modulation classification

Fingerprint Dive into the research topics of 'Modulation classification via gibbs sampling based on a latent dirichlet bayesian network'. Together they form a unique fingerprint.

Cite this