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.

该建议建立在我们之前针对Bernoulli(P)信源的LDPC联合信源信道编码工作的基础上，并且独立地建立在我们直接将数据的马尔可夫特性(记忆)包括到信道解码器的解码过程中的工作基础上。这两项工作都导致了多边型LDPC的设计和不同替代结构的性能比较-无论是更像Turbo的结构还是将相关性纳入Tanner图中。该建议的一个重要步骤是现在将这两个努力结合起来，这意味着设计一种实际的联合信源-信道编码多边型LDPC码，该码也适合数据中的马尔可夫特性，并将这些特性引入Tanner图中。预计代码优化将更多地受到不一致密度的影响，需要全密度演化，这通常被简化为仅考虑在一致情况下彼此依赖的方差或均值的演化。众所周知，全密度演化在实践中是非常苛刻的，但在这种情况下可能不可避免。然而，当然，这一步的必要性将得到评估。极性码是一种竞争的容量实现和低复杂度的代码构造，据我们所知，源记忆在那里还没有得到适当的处理，联合信源-信道编码似乎也总体上没有得到深入的研究。在这里，我们想研究两种选择，一种是将源相关性作为源极化的第一阶段，并相应地设计相应的极化源/信道编码方案。另一种选择建立在信源和信道编码部分的更强的集成或互连之上，以着眼于寻址用于信源极化的熵和用于信道极化的容量的鞅，当熵或容量降至特定阈值以下时对分量进行“穿孔”和“修剪”，然后分别将数据视为确定性的而将信道视为“冻结”的。