Exploring Capacity-Approaching Channel Codes In Distributed Source Coding

分布式信源编码（DSC），也称为分布式压缩或Slepian-Wolf问题，是指压缩两个或多个物理上分离但统计上相关的信息源，其中源将（压缩）数据发送到共同的目的地，而不相互通信。分布式信源编码与大量网络信息论问题密切相关，由于其无需传感器间通信就能压缩信源间冗余，因此对传感器网络特别有吸引力。这项研究的具体兴趣是使用强大的信道编码的实用和高效的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，而且其实际应用范围也非常广泛，因此，探索最先进的信道编码在这一新领域中所能提供的内容是特别令人兴奋的。本论文主要研究对称和非对称无记忆信源的无损和有损DSC的理论和算法。我们的目标是利用容量接近的线性信道码，包括涡轮码和低密度奇偶校验（LDPC）码，以实现压缩率接近理论极限的代码装箱的想法。特别注意具有非均匀分布和/或源相关性的源，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、码分裂、码率合并和后处理等技术，设计有效的编码策略来匹配信源。除了分析和模拟，实际问题也得到解决，并建立了一个硬件传感器测试平台，以验证和展示这些DSC原型的真实性能。

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.