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通信技术

Group Shuffled BP算法的密度演进和高斯近似

陈文, 刘斌彬, 白栋, Qihong Ge

Published Jan 1, 2010• 0 citations
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Abstract

为了分析Group Shuffled BP译码算法的收敛性能,同时简化密度演进计算的复杂度,在证明对称性条件的基础上,提出了基于Group Shuffled BP译码算法的密度演进的高斯近似。从而将密度演进中计算消息概率密度的无限维问题,简化为跟踪高斯分布均值的一维问题。仿真结果表明,该方法具有较高的精确度,可以有效分析Group Shuffled BP译码算法的收敛性能。

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为了分析Group Shuffled BP译码算法的收敛性能,同时简化密度演进计算的复杂度,在证明对称性条件的基础上,提出了基于Group Shuffled BP译码算法的密度演进的高斯近似。从而将密度演进中计算消息概率密度的无限维问题,简化为跟踪高斯分布均值的一维问题。仿真结果表明,该方法具有较高的精确度,可以有效分析Group Shuffled BP译码算法的收敛性能。
Shuffled-BP(SBP)译码算法是一种基于变量节点的串行消息传递译码算法，其收敛速度快于原有的置信度传播译码算法，然而由于实际工程实现中的半并行化处理，其收敛速度和误码性能均有所降低。为了进一步提高SBP算法的性能，该文提出一种交叠的Shuffled-BP(Overlapped Shuffled-BP, OSBP)译码算法。该算法采用若干个相同的子译码器以不同的更新顺序同时进行更新，对于每个变量节点，在每次迭代更新后选取最可靠的信息参与下一次迭代，以此提高迭代的收敛速度。理论分析和仿真实验均表明，在不增加额外存储空间的条件下，OSBP算法相比于SBP算法有着更优的误码性能以及更快的收敛速度。此外，提出的OSBP算法对于规则和不规则LDPC码均有效。
In this paper, we propose a method for enhancing performance of a sequential version of the belief-propagation (BP) decoding algorithm, the group shuffled BP decoding algorithm for low-density parity-check (LDPC) codes. An improved BP decoding algorithm, called the shuffled BP decoding algorithm, decodes each symbol node in serial at each iteration. To reduce the decoding delay of the shuffled BP decoding algorithm, the group shuffled BP decoding algorithm divides all symbol nodes into several groups. In contrast to the original group shuffled BP, which automatically generates groups according to symbol positions, in this paper we propose a method for grouping symbol nodes which generates groups according to the structure of a Tanner graph of the codes. The proposed method can accelerate the convergence of the group shuffled BP algorithm and obtain a lower error rate in a small number of iterations. We show by simulation results that the decoding performance of the proposed method is improved compared with those of the shuffled BP decoding algorithm and the group shuffled BP decoding algorithm.
Shuffled decoding is known to provide efficient and high-throughput implementation of LDPC decoders. In this paper, a new grouping technique of variable nodes to accelerate the message-passing rate is presented. In this algorithm, unlike other group-shuffled decoding methods, we consider for the first sub-group a set of variable nodes that has a low value of the intrinsic information, where the variable nodes of the code graph are divided into sub-groups called layers to perform group-by-group message-passing decoding and each iteration is broken into multiple sub-iterations. This paper proposes a simplification of the Variable Node group-shuffled Belief Propagation (Optimized VN group-shuffled BP) to lower the complexity of the check node update rule. Simulation results, verify that the Optimized VN group-shuffled BP does yield a faster convergence rate than both Belief Propagation (BP) and Check Node group-shuffled Belief Propagation (CN group-shuffled BP) decoder.