CIF: Medium: Collaborative Research: Spatially Coupled Sparse Codes on Graphs - Theory, Practice, and Extensions

CIF：媒介：协作研究：图上的空间耦合稀疏代码 - 理论、实践和扩展

• 批准号: 1161754
• 负责人: Daniel Costello
• 金额: $ 30.8万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1161754&HistoricalAwards=false
• 关键词: CIF; Medium; Collaborative; Research; Spatially

This research investigates a new approach to protecting the reliability of digital communication and digital storage systems. This approach takes advantage of recent work (by the research team and others) that formulates the "encoding" and "decoding" of data in terms of a novel graphical representation; this formulation has several advantages over existing techniques for insuring data integrity, including better performance at very low power and the absence of an 'error floor', i.e., the ability to consistently (and significantly) lower the decoded error probability with incremental expenditures of power. The ultimate goal of the research is more reliable delivery of digital data, text, computer files, speech and audio signals, video, etc. - using devices that require less power (and thus have longer battery life) and shorter processing delay.More specifically, the research investigates the use of spatially coupled sparse codes - channel (error control) codes with a sparse parity check representation formed by coupling together a chain of small "protographs". This approach, which was pioneered by the research team in the context of terminated low-density parity check convolutional codes, has recently been shown to possess a unique combination of properties - iterative decoding performance that approaches channel capacity and minimum distance that grows linearly with block length - as the code size gets large. The research follows four tracks: (1) the design and analysis of low latency/memory decoding strategies; (2) decoded error probability performance guarantees; (3) the development and analysis of spatially coupled sparse codes with algebraic structure; and (4) the application of spatial coupling outside the immediate domain of channel coding, including cooperative diversity, compressed sensing, and multi-terminal source/channel coding.

这项研究研究了一种保护数字通信和数字存储系统可靠性的新方法。 这种方法利用了最近的工作（由研究团队和其他人完成），该工作以新颖的图形表示形式制定了数据的“编码”和“解码”；与确保数据完整性的现有技术相比，该公式具有多个优点，包括在非常低的功耗下提供更好的性能以及不存在“错误底限”，即能够通过增加功耗来一致（且显着）降低解码错误概率。 该研究的最终目标是更可靠地传递数字数据、文本、计算机文件、语音和音频信号、视频等——使用需要更少电量（因此具有更长电池寿命）和更短处理延迟的设备。更具体地说，该研究研究了空间耦合稀疏码的使用——具有稀疏奇偶校验表示的通道（错误控制）码，通过将一串小“原图”耦合在一起形成。 这种方法是研究团队在终止低密度奇偶校验卷积码的背景下首创的，最近被证明具有独特的属性组合——随着代码大小变大，迭代解码性能接近通道容量，最小距离随块长度线性增长。 研究方向分为四个方向：（1）低延迟/内存解码策略的设计与分析； (2)解码错误概率性能保证； (3) 具有代数结构的空间耦合稀疏码的开发和分析； (4)在信道编码直接域之外的空间耦合应用，包括协作分集、压缩感知和多终端源/信道编码。