分数阶抽样下相关信源的有损计算
Lossy Computing of Correlated Sources with Fractional Sampling
摘要：
本文考虑的问题，有损压缩的两个相关的源，这两个都是观察到的编码器的函数的计算。由于观测成本的存在，编码器被允许仅观测来自两个源的样本的子集，其中这样的样本对的一部分可能重叠。率失真函数的特点是无记忆源，然后专门为高斯和二进制源选择的功能和二次和汉明失真度量，分别。示出的最佳测量重叠分数取决于由解码器计算的函数，源统计，包括相关性，和链路速率。特殊情况下，最佳重叠分数是最大或最小的可能值给定的采样预算，说明非平凡的性能权衡的采样策略的设计进行了讨论。最后，分析扩展到多跳设置与联合高斯源，其中每个编码器只能观察到一个源。
英文摘要：
This paper considers the problem of lossy compression for the computation of a function of two correlated sources, both of which are observed at the encoder. Due to presence of observation costs, the encoder is allowed to observe only subsets of the samples from both sources, with a fraction of such sample pairs possibly overlapping. The rate-distortion function is characterized for memory-less sources, and then specialized to Gaussian and binary sources for selected functions and with quadratic and Hamming distortion metrics, respectively. The optimal measurement overlap fraction is shown to depend on the function to be computed by the decoder, on the source statistics, including the correlation, and on the link rate. Special cases are discussed in which the optimal overlap fraction is the maximum or minimum possible value given the sampling budget, illustrating non-trivial performance trade-offs in the design of the sampling strategy. Finally, the analysis is extended to the multi-hop set-up with jointly Gaussian sources, where each encoder can observe only one of the sources.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
