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Lossless Coding for Distributed Streaming Sources

机译：分布式流源的无损编码

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摘要

Distributed source coding is traditionally viewed in a block coding context wherein all source symbols are known in advance by the encoders. However, many modern applications to which distributed source coding ideas are applied, are better modeled as having streaming data. In a streaming setting, source symbol pairs are revealed to separate encoders in real time and need to be reconstructed at the decoder with subject to some tolerable end-to-end delay. In this paper, a causal sequential random binning encoder is introduced and paired with maximum likelihood (ML) and universal decoders. The latter uses a novel weighted empirical suffix entropy decoding rule. We derive a lower bounds on the error exponent with delay for each decoder. We also provide upper bounds for the special case of streaming with decoder side information and discuss when upper and lower bounds match. We show that both ML and universal decoders achieve the same (positive) error exponents for all rate pairs inside the Slepian-Wolf achievable rate region. The dominant error events in streaming are different from those in block-coding and result in different exponents. Because the sequential random binning scheme is also universal over delays, the resulting code eventually reconstructs every source symbol correctly with probability one.
机译：传统上，在块编码上下文中查看分布式源编码，其中所有源符号都由编码器预先知道。但是，许多应用了分布式源编码思想的现代应用程序都可以更好地建模为具有流数据。在流设置中，源符号对将实时显示给单独的编码器，并且需要在解码器中进行重构，并且要承受一些可忍受的端到端延迟。本文介绍了一种因果顺序随机合并编码器，并与最大似然（ML）和通用解码器配对。后者使用一种新颖的加权经验后缀熵解码规则。我们为每个解码器导出了具有延迟的误差指数的下限。我们还为带有解码器辅助信息的特殊流情况提供了上限，并讨论了上下限何时匹配。我们表明，对于Slepian-Wolf可达到的速率区域内的所有速率对，ML和通用解码器均实现相同（正）误差指数。流中的主要错误事件不同于块编码中的主要错误事件，并导致不同的指数。由于顺序随机装箱方案在延迟方面也是通用的，因此最终的代码最终以概率1正确地重建了每个源符号。

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