Vector gaussian multiple description with individual and central receivers

Mua Wang; Pramod Viswanath

doi:10.1109/ISIT.2006.261544

Vector gaussian multiple description with individual and central receivers

Mua Wang, Pramod Viswanath

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

1 Scopus citations

Abstract

L multipla descriptions of a vector Gausslan source for individual and central receivers are investigated. The sum rate of the descriptions with covariance distortion measure constraints, in a positive semidefinite ordering, is exactly characterized. For two descriptions, the entire rate region is characterised, jointly Gaussian descriptions are optimal in achieving the limiting rases. The key component the solution is a novel information-theoretic inequality that is used to lower hound the achievable multiple description rates.

Original language	English (US)
Title of host publication	Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006
Pages	1589-1593
Number of pages	5
DOIs	https://doi.org/10.1109/ISIT.2006.261544
State	Published - 2006
Externally published	Yes
Event	2006 IEEE International Symposium on Information Theory, ISIT 2006 - Seattle, WA, United States Duration: Jul 9 2006 → Jul 14 2006

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
ISSN (Print)	2157-8101

Other

Other	2006 IEEE International Symposium on Information Theory, ISIT 2006
Country/Territory	United States
City	Seattle, WA
Period	7/9/06 → 7/14/06

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Information Systems
Modeling and Simulation
Applied Mathematics

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10.1109/ISIT.2006.261544

Cite this

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title = "Vector gaussian multiple description with individual and central receivers",

abstract = "L multipla descriptions of a vector Gausslan source for individual and central receivers are investigated. The sum rate of the descriptions with covariance distortion measure constraints, in a positive semidefinite ordering, is exactly characterized. For two descriptions, the entire rate region is characterised, jointly Gaussian descriptions are optimal in achieving the limiting rases. The key component the solution is a novel information-theoretic inequality that is used to lower hound the achievable multiple description rates.",

author = "Mua Wang and Pramod Viswanath",

year = "2006",

doi = "10.1109/ISIT.2006.261544",

language = "English (US)",

isbn = "1424405041",

series = "IEEE International Symposium on Information Theory - Proceedings",

pages = "1589--1593",

booktitle = "Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006",

note = "2006 IEEE International Symposium on Information Theory, ISIT 2006 ; Conference date: 09-07-2006 Through 14-07-2006",

}

Wang, M & Viswanath, P 2006, Vector gaussian multiple description with individual and central receivers. in Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006., 4036235, IEEE International Symposium on Information Theory - Proceedings, pp. 1589-1593, 2006 IEEE International Symposium on Information Theory, ISIT 2006, Seattle, WA, United States, 7/9/06. https://doi.org/10.1109/ISIT.2006.261544

Vector gaussian multiple description with individual and central receivers. / Wang, Mua; Viswanath, Pramod.
Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006. 2006. p. 1589-1593 4036235 (IEEE International Symposium on Information Theory - Proceedings).

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

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AB - L multipla descriptions of a vector Gausslan source for individual and central receivers are investigated. The sum rate of the descriptions with covariance distortion measure constraints, in a positive semidefinite ordering, is exactly characterized. For two descriptions, the entire rate region is characterised, jointly Gaussian descriptions are optimal in achieving the limiting rases. The key component the solution is a novel information-theoretic inequality that is used to lower hound the achievable multiple description rates.

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Vector gaussian multiple description with individual and central receivers

Abstract

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All Science Journal Classification (ASJC) codes

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