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Wyner-Ziv Type Versus Noisy Network Coding For a State-Dependent MAC

机译:Wyner-ziv类型与状态依赖的MAC的噪声编码

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We consider a two-user state-dependent multiaccess channel in which the states of the channel are known non-causally to one of the encoders and only strictly causally to the other encoder. Both encoders transmit a common message and, in addition, the encoder that knows the states non-causally transmits an individual message. We find explicit characterizations of the capacity region of this communication model. The analysis also reveals optimal ways of exploiting the knowledge of the state only strictly causally at the encoder that sends only the common message when such a knowledge is beneficial. The encoders collaborate to convey to the decoder a lossy version of the state, in addition to transmitting the information messages through a generalized Gel'fand-Pinsker binning. Particularly important in this problem are the questions of 1) optimal ways of performing the state compression and 2) whether or not the compression indices should be decoded uniquely. We show that both compression a-la noisy network coding, i.e., with no binning, and compression using Wyner-Ziv binning are optimal. The scheme that uses Wyner-Ziv binning shares elements with Cover and El Gamal original compress-and-forward, but differs from it mainly in that backward decoding is employed instead of forward decoding and the compression indices are not decoded uniquely. Finally, by exploring the properties of our outer bound, we show that, although not required in general, the compression indices can in fact be decoded uniquely essentially without altering the capacity region, but at the expense of larger alphabets sizes for the auxiliary random variables.
机译:我们考虑一个两个用户状态相关的多动力信道,其中信道的状态是非因果上的一个编码器,并且仅严格地因其他编码器而被告知。两个编码器都发送公共消息,另外,知道状态不因果传输单个消息的编码器。我们发现该通信模型的容量区域的明确表征。分析还揭示了利用状态的知识,只能严格导致对该状态的知识进行了最佳的方式,该编码器仅在这样的知识有益时发送常见消息。除了通过广义的Gel'Fand-Pinsker分布传输信息消息之外,编码器还与解码方式传达到解码器的损失版本。在这个问题中特别重要的是1)执行状态压缩的最佳方式和2)是否应该唯一地解码压缩指数。我们展示了压缩A-LA噪音网络编码,即没有搭档,使用Wyner-Ziv Binning的压缩是最佳的。使用Wyner-Ziv Binning的方案与封面和EL Gamal原始压缩和向前分享元素,但是从它的不同之处在于采用后向解码而不是向前解码而不是唯一地解码压缩指标。最后,通过探索我们的外部界限的属性,虽然通常不需要,但是,实际上可以在没有改变容量区域的情况下唯一地解码压缩指数,但是对于辅助随机变量的较大字母表尺寸的费用是唯一的。

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