An extremal inequality motivated by multiterminal information theoretic problems

Tie Liu; Pramod Viswanath

doi:10.1109/ISIT.2006.261881

An extremal inequality motivated by multiterminal information theoretic problems

Tie Liu, Pramod Viswanath

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

6 Scopus citations

Abstract

We prove a new extremal inequality, motivated by the vector Gaussian broadcast channel and the distributed source coding with a single quadratic distortion constraint problem. As a corollary, this inequality yields a generalization of the classical vector entropy-power inequality (EPI). As another corollary, this inequality sheds insight into maximizing differential entropy of a sum of jointly distributed random variables, generalizing a classical result of Cover and Zhang.

Original language	English (US)
Title of host publication	Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006
Pages	1016-1020
Number of pages	5
DOIs	https://doi.org/10.1109/ISIT.2006.261881
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.2006.261881

Cite this

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title = "An extremal inequality motivated by multiterminal information theoretic problems",

abstract = "We prove a new extremal inequality, motivated by the vector Gaussian broadcast channel and the distributed source coding with a single quadratic distortion constraint problem. As a corollary, this inequality yields a generalization of the classical vector entropy-power inequality (EPI). As another corollary, this inequality sheds insight into maximizing differential entropy of a sum of jointly distributed random variables, generalizing a classical result of Cover and Zhang.",

author = "Tie Liu and Pramod Viswanath",

year = "2006",

doi = "10.1109/ISIT.2006.261881",

language = "English (US)",

isbn = "1424405041",

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

pages = "1016--1020",

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",

}

Liu, T & Viswanath, P 2006, An extremal inequality motivated by multiterminal information theoretic problems. in Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006., 4036118, IEEE International Symposium on Information Theory - Proceedings, pp. 1016-1020, 2006 IEEE International Symposium on Information Theory, ISIT 2006, Seattle, WA, United States, 7/9/06. https://doi.org/10.1109/ISIT.2006.261881

An extremal inequality motivated by multiterminal information theoretic problems. / Liu, Tie; Viswanath, Pramod.
Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006. 2006. p. 1016-1020 4036118 (IEEE International Symposium on Information Theory - Proceedings).

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

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AU - Viswanath, Pramod

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AB - We prove a new extremal inequality, motivated by the vector Gaussian broadcast channel and the distributed source coding with a single quadratic distortion constraint problem. As a corollary, this inequality yields a generalization of the classical vector entropy-power inequality (EPI). As another corollary, this inequality sheds insight into maximizing differential entropy of a sum of jointly distributed random variables, generalizing a classical result of Cover and Zhang.

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M3 - Conference contribution

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BT - Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006

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ER -

An extremal inequality motivated by multiterminal information theoretic problems

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