LDPC- and Polar-coding-based Joint Source-Channel Coding

基于 LDPC 和 Polar 编码的联合源信道编码

• 批准号: 508333324
• 负责人: Professor Dr.-Ing. Werner Henkel
• 金额: --
• 依托单位: Department of Computer Science and Electrical Engineering
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/508333324?language=en
• 关键词: LDPC; Polar; coding; based; Joint

The proposal builds on our own previous works on LDPC joint source channel coding forBernoulli(p) sources and independently, on our works on directly including Markov properties (memory) of data into the decoding process of a channel decoder. Both works lead to multi-edge-type LDPC designs and performance comparisons for different alternative structures – be it more Turbo like or incorporating the dependencies into the Tanner graph.One important step in this proposal is to now join the two efforts, meaning designing an actual joint source-channel coding multi-edge-type LDPC code that is also suited for Markov properties in the data and introduces those properties into the Tanner graph. The code optimization is expected to be much more influenced by non-consistent densities, requiring full density evolution, which is usually simplified to just considering the evolution of the variance or the mean which are dependent on each other in the consistent case. Full density evolution is known to be very demanding in practice, but possibly unavoidable in this case. Nevertheless, of course, the necessity for this step will be evaluated.Polar codes are one of the competing capacity-achieving and low-complexity code constructions and, to our knowledge, source memory has not been properly treated there, yet, and joint source-channel coding seems also in general not thoroughly studied there. In here, we like to investigate two options, one to see source dependencies as a first stage of source polarization and design the corresponding polar source/channel coding scheme accordingly. The other option builds on a stronger integration or interlinking of the source and channel coding portions with a view onto the martingales addressing the entropy for the source polarization and the capacity for the channel polarization, “puncturing” and “pruning” components when entropies or capacities fall below certain thresholds, then seeing data as deterministic and channels as to be “frozen”, respectively.

该提案是基于我们以前的作品建立在LDPC联合源通道编码forbernoulli（P）源的基础上，并独立于我们的作品直接在包括Markov属性（Markov Properties（MOMEME））中，将数据（内存）纳入通道解码器的解码过程中。这两种作品都会导致多种替代结构的多种边缘类型的LDPC设计和性能比较 - 无论是更像还是将依赖项纳入坦纳图中。该提案的一个重要步骤是加入两项努力，这意味着将实际的联合源信号源编码编码的多个型号ldpc propie properties介绍到这些属性中，以适用于这些数据和数据介绍的属性。预计代码优化会受到非一致密度的影响，需要全密度演变，通常可以简化地考虑在一致的情况下仅考虑方差的演变或均值的演变。已知全密度演化在实践中非常苛刻，但在这种情况下可能不可避免。然而，当然，将评估此步骤的必要条件。光尔体代码是竞争能力方面和低复杂的代码构建体之一，据我们所知，在那里尚未对源存储器进行适当的处​​理，而且共同的源通道编码似乎一般都没有在那里进行彻底研究。在这里，我们想研究两个选项，一个将源依赖性视为源极化的第一阶段，并相应地设计相应的极性源/通道编码方案。另一个选项基于源和频道编码部分的更强集成或相互链接，并查看了Martingales，以解决源源极化的熵以及渠道极化的能力，“刺穿”和“修剪”组件时，当熵或容量的容量均低于某些阈值以下，然后将数据视为确定性和通道以“确定性和通道为“ frolozen”，相应地相应地相差。