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IEEE International Symposium on Information Theory (ISIT)

作者： Bilgen, Omer Wagner, Aaron B. Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

ISBN: (数字)9781728164328

ISBN: (纸本)9781728164328

We provide a method for outer bounding the rate-distortion region of Gaussian distributed compression problems in which the source variables can be embedded in a Gauss-Markov tree. The outer bound so obtained takes the form of a convex optimization problem. Simulations demonstrate that the outer bound is close to the Berger-Tung inner bound, coinciding with it in many cases.

关键词： multiterminal source-coding Gaussian sources logarithmic loss outer bound

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Rate Region of the One-Help-Two Quadratic Gaussian source-coding Problem With Markovity

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IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第3期69卷 2009-2032页

作者： Bilgen, Omer Wagner, Aaron B. Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA JPMorgan Asset Management London EC4Y 0JP England

We study the quadratic Gaussian one-help-two source-coding problem with Markovity, in which three encoders separately encode the components of a memoryless vector-Gaussian source that form a Markov chain and the central decoder aims to reproduce the first and the second components in the chain subject to individual mean-squared distortion constraints. We determine the rate region under a high-resolution assumption for the middle source.

关键词： multiterminal source-coding distributed compression Gaussian sources one-help-two problem network information theory

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A New Proof for the Quadratic Gaussian Two-Encoder source-coding Problem

A New Proof for the Quadratic Gaussian Two-Encoder Source-Co...

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IEEE International Symposium on Information Theory (ISIT)

作者： Bilgen, Omer Wagner, Aaron B. Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

This paper revisits the quadratic Gaussian two-encoder source-coding problem, for which a Gaussian quantizeand-bin scheme, also known as the Berger-Tung scheme, is known to achieve the entire rate region. We present a... 详细信息

ISBN: (纸本)9781538692912

关键词： source coding multiterminal source-coding Gaussian sources quantize-and-bin two-encoder problem

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The Quadratic Gaussian One-Help-Two source-coding Problem with Markovity

The Quadratic Gaussian One-Help-Two Source-Coding Problem wi...

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IEEE International Symposium on Information Theory (ISIT)

作者： Bilgen, Omer Wagner, Aaron B. Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

ISBN: (纸本)9781538647813

We consider the quadratic Gaussian one-help-two source-coding problem with Markovity, in which three encoders separately encode the components of a memoryless vector-Gaussian source that form a Markov chain and the central decoder aims to reproduce the first and the second components in the chain subject to individual distortion constraints. For this problem, we determine the minimum sum rate of the first and the second encoder given the distortion constraints and the rate of the third encoder. In particular, a simple scheme consisting of vector quantization followed by Slepian-Wolf binning achieves this minimum sum-rate. The proof of the converse draws from the quadratic Gaussian two-encoder source-coding problem, the Gaussian scalar-help-vector source-coding problem, and the Gaussian many-help-one source-coding problem.

关键词： Gaussian one-help-two problem multiterminal source-coding Gaussian sources Gaussian many-help-one problem Gaussian scalar-help-vector problem quantize-and-bin

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