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Vector Gaussian Multiple Description With Individual and Central Receivers

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2 Author(s)
Hua Wang ; Univ. of Illinois at Urbana-Champaign, Urbana ; Viswanath, P.

T multiple descriptions of a vector Gaussian 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 characterized. The key component of the solution is a novel information-theoretic inequality that is used to lower-bound the achievable multiple description rates. Jointly Gaussian descriptions are optimal in achieving the limiting rates. We also show the robustness of this description scheme: the distortions achieved are no larger when used to describe any non-Gaussian source with the same covariance matrix.

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Information Theory, IEEE Transactions on  (Volume:53 ,  Issue: 6 )