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

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

doi:10.1109/LSP.2014.2327193

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

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

Research output: Contribution to journal › Article › peer-review

16 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 language	English (US)
Article number	6822569
Pages (from-to)	1135-1139
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	21
Issue number	9
DOIs	https://doi.org/10.1109/LSP.2014.2327193
State	Published - 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

Access to Document

10.1109/LSP.2014.2327193

Cite this

@article{f93d241c53864896a369e722e3cc57af,

title = "Modulation classification via gibbs sampling based on a latent dirichlet bayesian network",

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.",

keywords = "Bayesian network, Gibbs sampling, latent dirichlet, modulation classification",

author = "Yu Liu and Osvaldo Simeone and Haimovich, {Alexander M.} and Wei Su",

year = "2014",

month = sep,

doi = "10.1109/LSP.2014.2327193",

language = "English (US)",

volume = "21",

pages = "1135--1139",

journal = "IEEE Signal Processing Letters",

issn = "1070-9908",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "9",

}

TY - JOUR

T1 - Modulation classification via gibbs sampling based on a latent dirichlet bayesian network

AU - Liu, Yu

AU - Simeone, Osvaldo

AU - Haimovich, Alexander M.

AU - Su, Wei

PY - 2014/9

Y1 - 2014/9

N2 - 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.

AB - 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.

KW - Bayesian network

KW - Gibbs sampling

KW - latent dirichlet

KW - modulation classification

UR - http://www.scopus.com/inward/record.url?scp=84901979264&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901979264&partnerID=8YFLogxK

U2 - 10.1109/LSP.2014.2327193

DO - 10.1109/LSP.2014.2327193

M3 - Article

AN - SCOPUS:84901979264

SN - 1070-9908

VL - 21

SP - 1135

EP - 1139

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

IS - 9

M1 - 6822569

ER -

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

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this