Title :
An Extremal Inequality Motivated by Multiterminal Information Theoretic Problems
Author :
Liu, Tie ; Viswanath, Pramod
Author_Institution :
Dept. of Electr. & Comput. Eng., Illinois Univ., Urbana, IL
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
Keywords :
Gaussian channels; broadcast channels; combined source-channel coding; entropy; differential entropy; distributed source coding; extremal inequality; multiterminal information theoretic problems; quadratic distortion constraint; vector Gaussian broadcast channel; vector entropy-power inequality; Additive noise; Broadcasting; Channel capacity; Constraint theory; Covariance matrix; Distributed computing; Entropy; Random variables; Source coding; Symmetric matrices;
Conference_Titel :
Information Theory, 2006 IEEE International Symposium on
Conference_Location :
Seattle, WA
Print_ISBN :
1-4244-0505-X
Electronic_ISBN :
1-4244-0504-1
DOI :
10.1109/ISIT.2006.261881