Computation Codes - A New Tool for Multi-user Communication

计算代码 - 多用户通信的新工具

• 批准号: 0830428
• 负责人: Michael Gastpar
• 金额: $ 27.5万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830428&HistoricalAwards=false
• 关键词: Computation; Codes; New; Tool; Multi

A major potential in large wireless networks is cooperation: A transmission from a single node is overheard not only by the intended receiver, but by all other nearby nodes; by analogy, any receiver not only captures the signal from the intended transmitter, but from all other nearby transmitters. The pessimist's perspective on these facts has shaped communication network designs of the past decades: Clever algorithms and protocols have been devised to avoid interference. Recent work, however, has revealed many scenarios under which interference turns out to be beneficial, provided it is suitably shaped. This is often referred to as physical-layer cooperation. Most of the cooperation schemes that have been proposed to date harvest the statistical dependence of the underlying signals. By contrast, in this project, novel codes are developed that permit to exploit the algebraic structure of the interference, enabling efficient and reliable computation of functions of the involved messages. Such codes will be referred to as computation codes. They are of independent interest in applications that explicitly call for computation, such as sensor networks.More generally, the computation coding perspective is used to develop a new framework for larger networks: Inside the network, judiciously chosen functions of the messages (rather than the messages themselves) are being passed around. As soon as a receiver has sufficiently many functions, it can infer the underlying message (i.e., the bits). This is reminiscent of so-called network coding, with the important difference that in the new framework, the question of which functions of the messages should be passed around is decided according to the actual interference characteristics, which can lead to significant gains.

大型无线网络的一个主要潜力是协作：来自单个节点的传输不仅会被预期的接收器监听，还会被所有其他附近的节点监听；以此类推，任何接收器不仅捕获来自预期发射器的信号，而且还捕获来自所有其他附近发射器的信号。悲观主义者对这些事实的看法塑造了过去几十年的通信网络设计：设计了巧妙的算法和协议来避免干扰。然而，最近的研究揭示了许多情况，在这些情况下，只要干扰得到适当的调整，干扰就会变得有益。这通常称为物理层协作。迄今为止提出的大多数合作方案都收集了基础信号的统计依赖性。相比之下，在该项目中，开发了新颖的代码，允许利用干扰的代数结构，从而能够有效且可靠地计算所涉及消息的函数。这样的代码将被称为计算代码。它们对明确需要计算的应用程序（例如传感器网络）具有独立的兴趣。更一般地，计算编码的角度用于为更大的网络开发新的框架：在网络内部，明智地选择消息的功能（而不是消息的功能）。消息本身）正在被传递。一旦接收器具有足够多的功能，它就可以推断出底层消息（即位）。这让人想起所谓的网络编码，重要的区别在于，在新框架中，消息的哪些功能应该被传递的问题是根据实际的干扰特性来决定的，这可以带来显着的收益。