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JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY

A Parallel Layered Decoding Algorithm for Non-layered LDPC Codes

Kun Guo, Yong Hei, Yumei Zhou, Shushan Qiao

Published Aug 27, 2010• 0 citations

Abstract

该文针对不可分层LDPC码无法利用分层算法进行译码的问题，提出了一种并行分层置信度传播(Parallel-Layered Belief-Propagation，PLBP)译码算法。与传统分层算法不同，该算法在译码时并行进行各层更新，串行进行层内各行更新。这种译码机制使得同一变量节点在各层内不同时进行更新，从而实现各变量节点在一次迭代中分层递进更新的算法目标。仿真表明，在不增加译码复杂度的情况下，该文提出的PLBP算法与传统的洪水算法相比，误码性能更优，而且所需要的平均迭代次数降低了约50%。此外，PLBP算法采用了合并的节点更新运算，最终使该算法达到的译码速度约为洪水算法的4倍。

DOI: 10.3724/sp.j.1146.2009.01160

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该文针对不可分层LDPC码无法利用分层算法进行译码的问题，提出了一种并行分层置信度传播(Parallel-Layered Belief-Propagation，PLBP)译码算法。与传统分层算法不同，该算法在译码时并行进行各层更新，串行进行层内各行更新。这种译码机制使得同一变量节点在各层内不同时进行更新，从而实现各变量节点在一次迭代中分层递进更新的算法目标。仿真表明，在不增加译码复杂度的情况下，该文提出的PLBP算法与传统的洪水算法相比，误码性能更优，而且所需要的平均迭代次数降低了约50%。此外，PLBP算法采用了合并的节点更新运算，最终使该算法达到的译码速度约为洪水算法的4倍。

The design of LDPC decoders with low complexity, high throughput, and good performance is a critical task. A well-known strategy is to design structured codes such as quasi- cyclic LDPC (QC-LDPC) that allow partially-parallel decoders. Sequential schedules, such as Layered Belief-Propagation (LBP), converge faster than the traditional flooding schedule while allowing parallel decoding of QC-LDPC codes. In this paper, we propose a novel low-complexity sequential schedule called Zigzag LBP (Z-LBP). Current LBP schedules do not allow partially- parallel architectures in the regime of high-rate codes with small- to-medium blocklengths. Our proposed algorithm can still be implemented in a partially-parallel manner in this regime. Z-LBP provides the same benefits as LBP including faster convergence speed and lower frame error rates than flooding.

摘要: 该文在现有译码算法的基础上提出一种高效的非二进制低密度奇偶校验码(NB-LDPC)译码方法，充分利用了分层译码算法与Min-max算法的优点，不但译码复杂度低、需要的存储空间小，而且可将译码速度提高一倍。应用该算法，对一种定义在GF(25)上的(620,509)码进行了仿真。该码的仿真结果表明：在相同误码率下，该文译码算法所需最大迭代次数仅为Zhang的算法(2011)的45%。关键词: 非二进制低密度奇偶校验码(NB-LDPC) / 分层译码 / Min-max / 准循环码

The Layered Belief Propagation L-BP algorithm is is a modified Belief Propagation BP algorithm, where the check nodes are divided in subgroups called layers and each iteration is broken into multiple sub-iterations. In this paper, we consider layered belief propagation decoding and propose an efficient variable node layering for decoding LDPC codes that performs well. We compare the performance of the first introduced LDPC decoding algorithm BP and Layered BP using check node layering with Layered BP using variable node layering in terms of bit error rate (BER). We show that the convergence for decoding LDPC codes is increasing by using a simple and efficient layering strategy.