...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Information Theory >A Probabilistic Peeling Decoder to Efficiently Analyze Generalized LDPC Codes Over the BEC
【24h】

A Probabilistic Peeling Decoder to Efficiently Analyze Generalized LDPC Codes Over the BEC

机译：一种概率去皮解码器，可通过BEC有效地分析通用LDPC码

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we analyze the tradeoff between coding rate and asymptotic performance of a class of generalized low-density parity-check (GLDPC) codes constructed by including a certain fraction of generalized constraint (GC) nodes in the graph. The rate of the GLDPC ensemble is bounded using classical results on linear block codes, namely, Hamming bound and Varshamov bound. We also study the impact of the decoding method used at GC nodes. To incorporate both bounded-distance (BD) and maximum likelihood (ML) decoding at GC nodes into our analysis without resorting on multi-edge type of degree distributions (DDs), we propose the probabilistic peeling decoding (P-PD) algorithm, which models the decoding step at every GC node as an instance of a Bernoulli random variable with a successful decoding probability that depends on both the GC block code and its decoding algorithm. The P-PD asymptotic performance over the BEC can be efficiently predicted using standard techniques for LDPC codes such as density evolution (DE) or the differential equation method. Furthermore, for a class of GLDPC ensembles, we demonstrate that the simulated P-PD performance accurately predicts the actual performance of the GLPDC code under ML decoding at GC nodes. We illustrate our analysis for GLDPC code ensembles with regular and irregular DDs. In all cases, we show that a large fraction of GC nodes is required to reduce the original gap to capacity, but the optimal fraction is strictly smaller than one. We then consider techniques to further reduce the gap to capacity by means of random puncturing, and the inclusion of a certain fraction of generalized variable nodes in the graph.
机译：在本文中，我们分析了通过在图中包括一定比例的广义约束（GC）节点构成的一类广义低密度奇偶校验（GLDPC）码的编码率和渐近性能之间的权衡。使用线性分组码的经典结果（即汉明绑定和瓦尔沙莫夫绑定）对GLDPC集成的速率进行限制。我们还研究了GC节点上使用的解码方法的影响。为了将GC节点上的有界距离（BD）和最大似然（ML）解码合并到我们的分析中，而无需借助多边类型的度数分布（DDs），我们提出了概率剥离解码（P-PD）算法，该算法将每个GC节点处的解码步骤建模为伯努利随机变量的实例，其成功解码概率取决于GC块代码及其解码算法。可以使用LDPC码的标准技术（例如密度演化（DE）或微分方程方法）有效地预测BEC上的P-PD渐近性能。此外，对于一类GLDPC集成，我们证明了模拟的P-PD性能可以准确预测GC节点在ML解码下的GLPDC代码的实际性能。我们说明了对具有规则和不规则DD的GLDPC代码集合的分析。在所有情况下，我们都表明需要很大一部分GC节点来减少原始的容量差距，但是最佳分数严格小于1。然后，我们考虑通过随机删余以及在图中包含一定比例的广义变量节点来进一步减少容量差距的技术。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号