Exploring Capacity-Approaching Channel Codes In Distributed Source Coding

探索分布式源编码中的容量逼近信道码

• 批准号: 0430634
• 负责人: Jing Li
• 金额: $ 10万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0430634&HistoricalAwards=false
• 关键词: Exploring; Capacity; Approaching; Channel; Codes

ABSTRACTDistributed source coding (DSC), also known as distributed compression or the Slepian-Wolf problem, refers to the compression of two or more physically separated but statistically correlated information sources, where the sources send the (compressed) data to a common destination without communicating to each other. Having a close relation to a wealth of network information theory problems, distributed source coding is particularly appealing to sensor networks due to its ability to compress out the inter-source redundancy without explicit inter-sensor communication. Of specific interest to this research is the algorithmic design of practical and efficient DSC solutions using powerful channel codes. In view of the fairly mature status of the advanced channel coding technologies in a theoretical context and the very pervasive scope of their well-proven practical applications, exploring what cutting-edge channel codes can offer in this new field is particularly exciting. This research focuses on the theoretic and algorithmic study of lossless and lossy DSC for symmetric and asymmetric memoryless sources. The goal is to exploit the code binning idea in capacity-approaching linear channel codes, including turbo codes and low-density parity-check (LDPC) codes, to achieve compression rates that are close to the theoretical limit. Specific attention is paid to sources that have non-uniform distributions and/or source-dependent correlations, which are common in practical applications like sensor networks and multimedia streaming. Techniques including source decomposition, code splitting, rate combining and post-processing are used to design efficient coding strategies to match to the sources. In addition to analysis and simulations, practical issues are also addressed and a hardware sensor testbed is built to verify and demonstrate the true performance of these DSC prototypes.

分布式信源编码(DSC)，也称为分布式压缩或Slepian-Wolf问题，指的是两个或更多物理上分离但统计上相关的信息源的压缩，其中源将(压缩的)数据发送到共同的目的地，而不相互通信。分布式信源编码与众多的网络信息论问题有着密切的联系，能够在不需要传感器间显式通信的情况下压缩信源间的冗余，因此对传感器网络特别有吸引力。这项研究特别感兴趣的是使用强大的信道码的实用和高效的DSC解决方案的算法设计。鉴于先进的信道编码技术在理论上已经相当成熟，而且其广泛的实际应用范围，探索尖端的信道编码在这个新的领域中能提供什么是特别令人兴奋的。本文主要对对称和非对称无记忆信源的无损和有损DSC进行了理论和算法研究。其目标是在接近容量的线性信道码(包括Turbo码和低密度奇偶校验(LDPC)码)中利用码组思想来获得接近理论极限的压缩比。特别关注具有非均匀分布和/或依赖于源的相关性的源，这在传感器网络和多媒体流等实际应用中很常见。采用信源分解、码流分割、码率合并和后处理等技术，设计了与信源匹配的高效编码策略。除了分析和仿真外，还解决了实际问题，并搭建了硬件传感器试验台来验证和演示这些DSC原型的真实性能。