具有保真度准则的单播多用户通信的通用操作理论
A universal, operational theory of unicast multi-user communication with fidelity criteria
摘要：
这是一个三部分的文件。当信道是Csiszar和Korner在他们的书中定义的复合信道和Verdu和Han定义的一般信道时，在保真度标准下通信的源信道分离的最优性在第I部分中被证明。假设随机码是允许的。本文标题中的“通用”一词，指的是渠道模式是复合型的。证明使用分层黑盒或分层输入输出视点。特别地，当证明分离时，仅使用能够在特定失真水平内传送源的信道的端到端描述。这意味着，只要存在源模型，通道模型就不会对分离起任何作用。进一步影响的分层黑盒的观点进行了讨论。在第二部分中证明了在单播设置中的一般复合介质上的多用户通信的源介质分离与保真度标准的最优性，从而将第一部分推广到单播，多用户设置。第三部分理解了这个问题，“为什么一个能够在一定失真水平内通信源的信道，也能够以低于将源编码到失真水平内所需的速率的下确界的任何速率来通信比特”：这是为什么具有保真度标准的通信的最优分离的核心。获得这种理解的视角是一个随机覆盖包装的视角，证明是可操作的。
英文摘要：
This is a three part paper. Optimality of source-channel separation for communication with a fidelity criterion when the channel is compound as defined by Csiszar and Korner in their book and general as defined by Verdu and Han, is proved in Part I. It is assumed that random codes are permitted. The word "universal" in the title of this paper refers to the fact that the channel model is compound. The proof uses a layered black-box or a layered input-output view-point. In particular, only the end-to-end description of the channel as being capable of communicating a source to within a certain distortion level is used when proving separation. This implies that the channel model does not play any role for separation to hold as long as there is a source model. Further implications of the layered black-box view-point are discussed. Optimality of source-medium separation for multi-user communication with fidelity criteria over a general, compound medium in the unicast setting is proved in Part II, thus generalizing Part I to the unicast, multi-user setting. Part III gets to an understanding of the question, "Why is a channel which is capable of communicating a source to within a certain distortion level, also capable of communicating bits at any rate less than the infimum of the rates needed to code the source to within the distortion level": this lies at the heart of why optimality of separation for communication with a fidelity criterion holds. The perspective taken to get to this understanding is a randomized covering-packing perspective, and the proof is operational.
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。
