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A hybrid random-structured coding scheme for the Gaussian two-terminal source coding problem under a covariance matrix distortion constraint

机译:协方差矩阵畸变约束下高斯两端信源编码问题的混合随机结构编码方案

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This paper focuses on the Gaussian two-terminal source coding problem under a covariance matrix distortion constraint, which subsumes the quadratic Gaussian two-terminal source coding problem with individual distortion constraints, whose complete solution is known. Different from existing schemes which are either random or structured, we propose a new hybrid random-structured scheme with a sum-rate strictly smaller than the quantize-and-bin (QB) upper bound in certain cases. The first layer of our scheme is a QB random coding scheme attempting to achieve an intermediate distortion matrix that is as symmetric as possible. The second layer is a structured scheme that targets at reconstructing a weighted difference of the observed sources conditioned on their quantized versions in the first layer. We prove that the gap between the sum-rate of our scheme and its lower bound is no larger than two bits per sample, in particular, this gap decreases to exactly one bit per sample when the source covariance matrix is symmetrifiable in the sense that the intermediate covariance matrix can be made purely symmetric.
机译:本文重点研究协方差矩阵失真约束下的高斯两端源编码问题,该问题包含具有单个失真约束的二次高斯两端源编码问题,其完整解是已知的。与现有的随机或结构化方案不同,我们提出了一种新的混合随机结构化方案,其总速率在某些情况下严格小于量化和合并(QB)上限。我们方案的第一层是QB随机编码方案,试图获得尽可能对称的中间失真矩阵。第二层是一种结构化方案,其目标是在第一层中重建以其量化版本为条件的观测源的加权差异。我们证明了我们的方案的总和率与其下限之间的差距不大于每个样本两位,尤其是当源协方差矩阵是对称的时,该差距减小到每个样本正好一位可以使中间协方差矩阵完全对称。

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