An information theoretic perspective over an extremal entropy inequality

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper focuses on developing an alternative proof for an extremal entropy inequality, originally presented in [1]. The proposed alternative proof is simply based on the classical entropy power inequality and the data processing inequality. Compared with the proofs in [1], the proposed alternative proof is simpler, more direct, and information theoretic, and presents the advantage of providing the structure of the optimal solution covariance matrix. Also, the proposed proof might also be used as a novel method to address applications such as calculation of the vector Gaussian broadcast channel capacity, establishing a lower bound for the achievable rate of distributed source coding with a single quadratic distortion constraint, and the secrecy capacity of the Gaussian wire-tap channel.

Original languageEnglish
Title of host publication2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012
Pages1266-1270
Number of pages5
DOIs
Publication statusPublished - 22 Oct 2012
Event2012 IEEE International Symposium on Information Theory, ISIT 2012 - Cambridge, MA, United States
Duration: 1 Jul 20126 Jul 2012

Publication series

NameIEEE International Symposium on Information Theory - Proceedings

Other

Other2012 IEEE International Symposium on Information Theory, ISIT 2012
CountryUnited States
CityCambridge, MA
Period1/7/126/7/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modelling and Simulation
  • Applied Mathematics

Fingerprint Dive into the research topics of 'An information theoretic perspective over an extremal entropy inequality'. Together they form a unique fingerprint.

  • Cite this

    Park, S., Serpedin, E., & Qaraqe, K. (2012). An information theoretic perspective over an extremal entropy inequality. In 2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012 (pp. 1266-1270). [6283060] (IEEE International Symposium on Information Theory - Proceedings). https://doi.org/10.1109/ISIT.2012.6283060