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北京航空航天大学学报

改进的基于剩余度置信度传播的 LDPC 解码算法

赵明, 张晓林

Published Jan 1, 2014• 0 citations
OpenAlex

Abstract

低密度奇偶校验（LDPC，Low—Density Parity—Check）码的剩余度置信度传播（RBP，Residual Belief—Propagation）和基于行的剩余度置信度传播（NWRBP，Node-Wise RBP）解码算法的性能提升非常有限且计算复杂度较高．提出改进的RBP（ERBP，Enhanced RBP）算法，在一个子迭代中，仅更新一个消息，然后设置被更新消息所在行的所有节点的剩余度值为0，使得ERBP解码算法在每个子迭代中使用不同行的消息进行计算，以加速迭代收敛．不同的LDPC码用于对所提出的算法进行性能仿真．仿真结果表明，与其他算法相比，ERBP算法降低了误帧率（FER,Frame Error Ratio）,并加快了迭代收敛速度。

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IET Communications•Jul 17, 2017•1 citation
低密度奇偶校验（LDPC，Low—Density Parity—Check）码的剩余度置信度传播（RBP，Residual Belief—Propagation）和基于行的剩余度置信度传播（NWRBP，Node-Wise RBP）解码算法的性能提升非常有限且计算复杂度较高．提出改进的RBP（ERBP，Enhanced RBP）算法，在一个子迭代中，仅更新一个消息，然后设置被更新消息所在行的所有节点的剩余度值为0，使得ERBP解码算法在每个子迭代中使用不同行的消息进行计算，以加速迭代收敛．不同的LDPC码用于对所提出的算法进行性能仿真．仿真结果表明，与其他算法相比，ERBP算法降低了误帧率（FER,Frame Error Ratio）,并加快了迭代收敛速度。
低密度奇偶校验(low-density parity check, LDPC)码的校验节点通常采用单奇偶校验(single parity check, SPC)码，然而当采用一种具有更强纠错能力分量码替换LDPC码中的SPC码时可以构造出一种性能更好的广义LDPC(generalized LDPC, GLDPC)码。鉴于此，采用一个(17, 9)平方剩余(quadratic residue, QR)码作为分量码来替换LDPC中的SPC码构造出了一种基于QR码的GLDPC码。通过研究GLDPC码和QR码的构造以及GLDPC码的译码算法，提出了一种基于(17, 9) QR码的GLDPC码构造方法，研究了该GLDPC码的性能，并对该GLDPC码与传统的LDPC码、同码率不同码长的GLDPC码以及同码长不同码率的GLDPC码进行了性能仿真。仿真结果表明，基于(17, 9)QR码的GLDPC码相比同码率下的LDPC码，在错误比特率和译码收敛速度上都取得了更优异的表现。
针对对数域和积LDPC(Low Density Parity Check Codes)译码算法复杂度高和译码采用的SMP(Standard Message Passing)调度算法收敛速度慢的问题,提出基于RMP(RowMessage Passing)调度的简化差分域LDPC译码算法.用RMP调度优化变量节点信息更新使收敛速度提高一倍.由于在Tanner图上传递的是概率的差分值,并且在校验节点信息更新时选择w个绝对值最小的差分值进行,降低了算法复杂度.实验表明本文算法在不降低译码性能条件下降低了算法复杂度,提高了算法收敛速度.
Low-density parity-check (LDPC) codes are favorable in modern telecommunication technologies due to its superior error-correction capability especially when the codeword length is large. The commonly-used LDPC decoders are based on the belief-propagation (BP) algorithms. It is well known that the layered (serial) belief-propagation (LBP) decoding algorithms can reduce the number of iterations by half in comparison with the flooding (parallel) BP decoding algorithms. Further reduction in total number of iterations can be achieved by using the informed dynamic scheduling techniques, such as residual BP (RBP) and node-wise residual BP (NWRBP) methods. However, the incurred additional computation of residuals is far from trivial. In this paper, we propose a novel efficient dynamic scheduling scheme, called a posteriori probability RBP (APPRBP) algorithm, which offers more flexibility to leverage the performance-complexity trade-off using a threshold parameter. The simulation results demonstrate that the average iteration numbers for our proposed APPRBP algorithm can be remarkably reduced especially in the low E_b/N_0 (signal-energy-per-information-bit to noise-power-spectral-density ratio) conditions while the bit-error rate (BER) performance is subject to slight degradation.
Low-density parity-check (LDPC) codes are favorable in modern telecommunication technologies due to its superior error-correction capability especially when the codeword length is large. The commonly-used LDPC decoders are based on the belief-propagation (BP) algorithms. It is well known that the layered (serial) belief-propagation (LBP) decoding algorithms can reduce the number of iterations by half in comparison with the flooding (parallel) BP decoding algorithms. Further reduction in total number of iterations can be achieved by using the informed dynamic scheduling techniques, such as residual BP (RBP) and node-wise residual BP (NWRBP) methods. However, the incurred additional computation of residuals is far from trivial. In this paper, we propose a novel efficient dynamic scheduling scheme, called a posteriori probability RBP (APPRBP) algorithm, which offers more flexibility to leverage the performance-complexity trade-off using a threshold parameter. The simulation results demonstrate that the average iteration numbers for our proposed APPRBP algorithm can be remarkably reduced especially in the low E_b/N_0 (signal-energy-per-information-bit to noise-power-spectral-density ratio) conditions while the bit-error rate (BER) performance is subject to slight degradation.
LDPC码分层译码算法在进行整数量化操作时，存储单元的限制会导致译码信息的溢出。本文系统分析了溢出错误的原因，并提出了两种改进的分层译码方案，错误部分消除方案（ partially eliminating errors scheme，PEES）和不同比特量化方案（different bit quantization scheme，DBQS）。两种改进方案分别从消除部分错误和避免溢出错误的角度来改进译码性能，且硬件实现时只需增加一定数量的加法器和移位操作。通过对不同码长、不同量化比特的LDPC码进行仿真，结果表明，2种方案均有效地抑制了溢出错误，与基于全精度浮点数运算的修正算法相比，在误码率为1．0＆#215;10－4时，分别仅有约1 dB和1．8 dB的性能损耗。
A new decoding strategy of belief propagation (BP) for low‐density parity‐check codes is presented by serially concatenating the traditional simultaneous decoding (also known as flooding) and the informed dynamic decoding (IDD) (e.g. node‐wise residual belief propagation (NW RBP) or informed variable‐to‐check (IVC) RBP). The frame error rates (FER) and bit error rates (BER) of the concatenated BP decoder outperform that of the non‐concatenated single decoder. In addition, as the signal‐to‐noise ratio (SNR) increases, the average number of decoding iterations required for the concatenated BP decoder decreases, and tends to merge with that of the first sub‐decoder in the high SNR region. Moreover, since the IDD strategies concerned in this paper require extra effort for the residual computation, and the sequential updating of IDD is much more time consuming than the simultaneous updating of flooding. Therefore, compared with using a single IDD, serially concatenating flooding and IDD reduces both the approximate decoding complexity and the decoding latency because the concatenated decoder is still dominated by the sub‐decoder flooding.