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The Gaussian Many-Help-One Distributed Source Coding Problem

机译:高斯多帮助一分布式源编码问题

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Jointly Gaussian memoryless sources (y1,...,yN) are observed at N distinct terminals.The goal is to efficiently encode the observations in a distributed fashion so as to enable reconstruction of any one of the observations, say y1, at the decoder subject to a quadratic fidelity criterion.Our main result is a precise characterization of the rate-distortion region when the covariance matrix of the sources satisfies a "tree-structure" condition. In this situation, a natural analog/digital separation scheme optimally trades off the distributed quantization rate tuples and the distortion in reconstruction: each encoder consists of a point-to-point vector quantizer followed by a Slepian-Wolf binning encoder.
机译:在N个不同的终端上观察到联合高斯无记忆源(y1,...,yN),目标是以分布式方式有效地对观察进行编码,以便在解码器处重构任何一个观察,例如y1我们的主要结果是,当源的协方差矩阵满足“树形结构”条件时,精确表征速率失真区域。在这种情况下,自然的模拟/数字分离方案可以最佳地权衡分布的量化速率元组和重构中的失真:每个编码器均由一个点对点矢量量化器以及一个Slepian-Wolf binning编码器组成。

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