Channel estimation by inference on Gaussian Markov random fields

Thomas J. Riedl; Jun Won Choi; Andrew C. Singer

doi:10.1109/ACSSC.2009.5470038

Channel estimation by inference on Gaussian Markov random fields

Thomas J. Riedl
, Jun Won Choi
, Andrew C. Singer

Office of the Dean of the College of Engineering and Applied Sciences

University of Illinois at Urbana-Champaign

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this paper, we discuss a novel method for channel estimation. The approach is based on the idea of modeling the complex channel gains by a Markov random field. This graphical model is used to capture the statistical dependencies between consecutive taps in time and delay. The sum-product algorithm is finally employed to infer a MAP channel estimate from given observations.

Original language	English
Title of host publication	Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers
Pages	1007-1011
Number of pages	5
DOIs	https://doi.org/10.1109/ACSSC.2009.5470038
State	Published - 2009
Event	43rd Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: Nov 1 2009 → Nov 4 2009

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Conference

Conference	43rd Asilomar Conference on Signals, Systems and Computers
Country/Territory	United States
City	Pacific Grove, CA
Period	11/1/09 → 11/4/09

Keywords

Channel estimation
Channel modeling
Gaussian message passing
Markov random field

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10.1109/ACSSC.2009.5470038

Cite this

@inproceedings{8fd00e81afe547fab3fd1bf8cde38c1a,

title = "Channel estimation by inference on Gaussian Markov random fields",

abstract = "In this paper, we discuss a novel method for channel estimation. The approach is based on the idea of modeling the complex channel gains by a Markov random field. This graphical model is used to capture the statistical dependencies between consecutive taps in time and delay. The sum-product algorithm is finally employed to infer a MAP channel estimate from given observations.",

keywords = "Channel estimation, Channel modeling, Gaussian message passing, Markov random field",

author = "Riedl, \{Thomas J.\} and Choi, \{Jun Won\} and Singer, \{Andrew C.\}",

year = "2009",

doi = "10.1109/ACSSC.2009.5470038",

language = "English",

isbn = "9781424458271",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

pages = "1007--1011",

booktitle = "Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers",

note = "43rd Asilomar Conference on Signals, Systems and Computers ; Conference date: 01-11-2009 Through 04-11-2009",

}

Riedl, TJ, Choi, JW & Singer, AC 2009, Channel estimation by inference on Gaussian Markov random fields. in Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers., 5470038, Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 1007-1011, 43rd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, United States, 11/1/09. https://doi.org/10.1109/ACSSC.2009.5470038

Channel estimation by inference on Gaussian Markov random fields. / Riedl, Thomas J.; Choi, Jun Won; Singer, Andrew C.
Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers. 2009. p. 1007-1011 5470038 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Channel estimation by inference on Gaussian Markov random fields

AU - Riedl, Thomas J.

AU - Choi, Jun Won

AU - Singer, Andrew C.

PY - 2009

Y1 - 2009

N2 - In this paper, we discuss a novel method for channel estimation. The approach is based on the idea of modeling the complex channel gains by a Markov random field. This graphical model is used to capture the statistical dependencies between consecutive taps in time and delay. The sum-product algorithm is finally employed to infer a MAP channel estimate from given observations.

AB - In this paper, we discuss a novel method for channel estimation. The approach is based on the idea of modeling the complex channel gains by a Markov random field. This graphical model is used to capture the statistical dependencies between consecutive taps in time and delay. The sum-product algorithm is finally employed to infer a MAP channel estimate from given observations.

KW - Channel estimation

KW - Channel modeling

KW - Gaussian message passing

KW - Markov random field

UR - https://www.scopus.com/pages/publications/77953834491

U2 - 10.1109/ACSSC.2009.5470038

DO - 10.1109/ACSSC.2009.5470038

M3 - Conference contribution

AN - SCOPUS:77953834491

SN - 9781424458271

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 1007

EP - 1011

BT - Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers

T2 - 43rd Asilomar Conference on Signals, Systems and Computers

Y2 - 1 November 2009 through 4 November 2009

ER -

Channel estimation by inference on Gaussian Markov random fields

Abstract

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