基于平均概率和停止准则的多元LDPC码加权符号翻转译码算法

为了提高多元低密度奇偶校验(LDPC, low density parity-check)码符号翻转译码算法的性能并降低译码的复杂度,提出了基于平均概率和停止准则的多元LDPC码加权符号翻转译码(APSCWSF, average probability and stopping crite-rion weighted symbol flipping)算法。该算法将校验节点邻接符号节点的平均概率信息作为权重,使翻转函数更加有效,提高符号的翻转效率,进而改善译码性能。并且通过设置迭代停止准则进一步加快算法的收敛速度。仿真结果显示,在加性高斯白噪声信道下,误符号率为10?5时,相比WSF算法、NSCWSF算法(Osc=10)和NSCWSF算法(Osc=6),APSCWSF算法(Osc=10)分别获得约0.68 dB、0.83 dB和0.96 dB的增益。同时,APSCWSF算法(Osc=6)的平均迭代次数也分别降低78.60% ~79.32%、74.89% ~ 75.95% 和67.20% ~70.80%。     

基于联合判决消息传递机制的LDPC码译码算法研究

采用消息传递算法(Message passing algorithm)对LDPC码进行译码时,变量消息的振荡会引起错误的发生.本文以(600.300)非规则LDPC码仿真实验为例分析了不同译码效果下判决消息均值的分布特点,并结合环的特点,分析了译码产生错误判决的原因.研究了"纠删"型消息传递机制和联合判决迭代停止准则,针对判决消息出现振荡情况,提出以"纠删"方式处理变量消息的更新,并结合变量节点判决消息均值分布趋势与伴随式结果确定迭代终止条件.在此基础上,提出一种新的LDPC码译码算法.仿真分析表明,新的译码算法能够在减少迭代次数和降低译码复杂度的同时,有效提高译码的纠错性能.     

近地应用CCSDS标准LDPC码动态补偿译码算法研究

针对CCSDS推荐的(8176,7154)有限几何准循环LDPC码,研究了LDPC码的修正最小和译码算法,提出了一种新的动态补偿最小和译码算法,并将本算法和修正最小和译码算法进行了性能比较。仿真结果显示,动态补偿最小和译码虽然算法迭代的收敛速度有所减慢,但具有比修正最小和算法更好的误码性能。     

基于ACE的准循环LDPC码构造

介绍了LDPC码的结构类型和译码实现,分析了环的连接性对误码性能的影响,详细阐述了停止集、EMD(Extrinsic Message Degree)、ACE(Approximate Cycle EMD)的关系,之后提出一种基于ACE的准循环LDPC码的构造方法,该方法可最大化围长和小停止集.仿真证明该方法具有良好的性能.     

影响多进制LDPC码译码性能因素研究

多进制LDPC码是将二进制LDPC码推广到有限域GF(q),其校验矩阵的元素不再是0和1,而是集合(0,1,2,…,q-1),译码仍然采用高效的基于置信度传播的迭代译码算法。文中主要阐述了准循环多进制LDPC码(QC-LDPC)校验矩阵的构造以及最小和译码算法的原理,然后在高斯白噪声信道(AWGN)中,用Matlab了仿真不同条件下LDPC码的译码性能,比较分析了影响多进制LDPC码译码性能的因素。     

基于新停止准则的多进制LDPC码加权符号翻转译码算法

为了降低多进制低密度奇偶校验(Low-Density Parity-Check,LDPC)码译码算法的复杂度,该文提出了基于新停止准则的符号翻转译码算法。该算法根据翻转函数和接收比特可靠性度量来确定对应的翻转符号,通过分析不满足校验方程个数的变化趋势来提前终止迭代。仿真结果表明,新算法在保持原有符号翻转译码算法误码性能不变的情况下,极大地减少了译码迭代次数,取得了译码性能和复杂度的折衷。     

一种改进的自纠正最小和LDPC码的译码算法

低密度奇偶校验(LDPC)码是一类具有优良纠错能力的差错控制编码,可以逼近香农极限.目前LDPC码正在进入越来越多的工程应用中,高效的译码算法具有重要的价值.在研究已知的LDPC码译码算法的基础上,提出了一种改进的简化译码算法,称为加约束的自纠正最小和(CSCMS)算法,该算法的计算复杂度与最小和(MS)译码算法相当,性能却提升了0.2 dB左右,与其他几种改进的简化译码算法相比,性能提升约0.1 dB,并且译码的平均迭代次数也有所降低.     

一种简单的Turbo码的迭代停止判据

Turbo码提出之后,由于其优异的性能成为研究热点。但为了得到其优异的性能,在译码过程中需要进行多次迭代,这造成巨大的译码延时。在不影响性能的情况下,为减少译码延时,迭代停止在Turbo码迭代译码过程中十分重要。提出一种基于似然比绝对值大于某一门限的比特数目的迭代停止判据。并且给出平均迭代次数、译码性能的仿真。仿真结果证明该算法的有效性。     

LDPC码加权比特翻转译码算法的低复杂度提前停止准则

近年来,针对LDPC码置信传播(BP)译码算法的提前停止准则的研究已经有了很多,但设计适合加权比特翻转(WBF)译码算法的提前停止准则却研究甚少。依据对WBF算法的全新理解方式,该文提出一种实现简单、适用性强的WBF算法提前停止准则,它能在译码的初始阶段检测绝大多数不可纠错的帧。仿真结果表明,基于提前停止准则的WBF算法在性能损失可以忽略的条件下,极大地降低迭代次数,在实现复杂度和性能之间达到了很好的折中。     

光通信乘性噪声信道基于LDPC码的SNR估计方法

针对目前现有的信噪比(SNR)估计 方法不适用光通信中乘性噪声信道的问题,分析了乘性噪声信道下,SNR失配 对低密度奇偶校验(LDPC)码性能的影响,并通过仿真表明乘性噪声信道下 SNR精确估计的必要性。提出了一种基于量化的LDPC 码判决反馈的SNR估计方法,首先对接收到的光信号进行量化,利用 简化后的期望最大(EM)算法对量化后的SNR 进行一次粗估计,接着利用基于LDPC迭代译码的判决反馈结果对 SNR进行精估计。仿真结果表明,本方法能够在接收 信号的均值和噪声方差等参数未知情况下,能有效完成LDPC码迭代译码的辅助 工作;在误码率(BER)为10－5时,SNR估计后 ,LDPC码的译码性能距离理想情况下的译码性能,仅有约0.12dB以内损失。     

On the stopping distance of finite geometry LDPC codes

In this letter, the stopping sets and stopping distance of finite geometry LDPC (FG-LDPC) codes are studied. It is known that FG-LDPC codes are majority-logic decodable and a lower bound on the minimum distance can be thus obtained. It is shown in this letter that this lower bound on the minimum distance of FG-LDPC codes is also a lower bound on the stopping distance of FG-LDPC codes, which implies that FG-LDPC codes have considerably large stopping distance. This may explain in one respect why some FG-LDPC codes perform well with iterative decoding in spite of having many cycles of length 4 in their Tanner graphs.     

Stopping set distribution of LDPC code ensembles

Stopping sets determine the performance of low-density parity-check (LDPC) codes under iterative decoding over erasure channels. We derive several results on the asymptotic behavior of stopping sets in Tanner-graph ensembles, including the following. An expression for the normalized average stopping set distribution, yielding, in particular, a critical fraction of the block length above which codes have exponentially many stopping sets of that size. A relation between the degree distribution and the likely size of the smallest nonempty stopping set, showing that for a /spl radic/1-/spl lambda/'(0)/spl rho/'(1) fraction of codes with /spl lambda/'(0)/spl rho/'(1)<1, and in particular for almost all codes with smallest variable degree >2, the smallest nonempty stopping set is linear in the block length. Bounds on the average block error probability as a function of the erasure probability /spl epsi/, showing in particular that for codes with lowest variable degree 2, if /spl epsi/ is below a certain threshold, the asymptotic average block error probability is 1-/spl radic/1-/spl lambda/'(0)/spl rho/'(1)/spl epsi/.     

基于停止集的喷泉编码有限长性能估计

喷泉编码是一类基于删除信道、面向数据分组的前向纠错编码技术。该文分析了停止集的尺度分布对固定码率喷泉编码解码性能的影响,提出了一种估算低误码条件下喷泉编码有限长性能的方法以及一种低复杂度的停止集尺度分布搜索算法。比较结果表明,该文给出的喷泉码解码性能上下界与实际仿真结果非常接近。     

Finding All Small Error-Prone Substructures in LDPC Codes

It is proven in this work that it is NP-complete to exhaustively enumerate small error-prone substructures in arbitrary, finite-length low-density parity-check (LDPC) codes. Two error-prone patterns of interest include stopping sets for binary erasure channels (BECs) and trapping sets for general memoryless symmetric channels. Despite the provable hardness of the problem, this work provides an exhaustive enumeration algorithm that is computationally affordable when applied to codes of practical short lengths n ap 500. By exploiting the sparse connectivity of LDPC codes, the stopping sets of size les 13 and the trapping sets of size les11 can be exhaustively enumerated. The central theorem behind the proposed algorithm is a new provably tight upper bound on the error rates of iterative decoding over BECs. Based on a tree-pruning technique, this upper bound can be iteratively sharpened until its asymptotic order equals that of the error floor. This feature distinguishes the proposed algorithm from existing non-exhaustive ones that correspond to finding lower bounds of the error floor. The upper bound also provides a worst case performance guarantee that is crucial to optimizing LDPC codes when the target error rate is beyond the reach of Monte Carlo simulation. Numerical experiments on both randomly and algebraically constructed LDPC codes demonstrate the efficiency of the search algorithm and its significant value for finite-length code optimization.     

多进制LDPC译码算法的研究

多进制LDPC码是将二进制LDPC码推广到有限域GF（q）,其校验矩阵的元素不再是（0,1）,而是集合（0,1,…,q-1）,译码仍然采用高效的基于置信度传播的迭代译码算法。这里主要推导了多进制译码算法的迭代公式,分析证明了基于快速傅里叶变换（FFT）理论的改进算法,最后通过仿真手段验证和分析了基于FFT的多进制译码算法的优越性能。     

Turbo Decoding on the Binary Erasure Channel: Finite-Length Analysis and Turbo Stopping Sets

This paper is devoted to the finite-length analysis of turbo decoding over the binary erasure channel (BEC). The performance of iterative belief-propagation decoding of low-density parity-check (LDPC) codes over the BEC can be characterized in terms of stopping sets. We describe turbo decoding on the BEC which is simpler than turbo decoding on other channels. We then adapt the concept of stopping sets to turbo decoding and state an exact condition for decoding failure. Apply turbo decoding until the transmitted codeword has been recovered, or the decoder fails to progress further. Then the set of erased positions that will remain when the decoder stops is equal to the unique maximum-size turbo stopping set which is also a subset of the set of erased positions. Furthermore, we present some improvements of the basic turbo decoding algorithm on the BEC. The proposed improved turbo decoding algorithm has substantially better error performance as illustrated by the given simulation results. Finally, we give an expression for the turbo stopping set size enumerating function under the uniform interleaver assumption, and an efficient enumeration algorithm of small-size turbo stopping sets for a particular interleaver. The solution is based on the algorithm proposed by Garello et al. in 2001 to compute an exhaustive list of all low-weight codewords in a turbo code.     

LDPC Codes Based on Latin Squares: Cycle Structure, Stopping Set, and Trapping Set Analysis

It is well known that certain combinatorial structures in the Tanner graph of a low-density parity-check (LDPC) code exhibit a strong influence on its performance under iterative decoding. These structures include cycles, stopping/trapping sets, and parameters such as the diameter of the code. In general, it is very hard to find a complete characterization of such configurations in an arbitrary code, and even harder to understand the intricate relationships that exist between these entities. It is, therefore, of interest to identify a simple setting in which all the described combinatorial structures can be enumerated and studied within a joint framework. One such setting is developed in this paper, for the purpose of analyzing the distribution of short cycles and the structure of stopping and trapping sets in Tanner graphs of LDPC codes based on idempotent and symmetric Latin squares. The parity-check matrices of LDPC codes based on Latin squares have a special form that allows for connecting combinatorial parameters of the codes with the number of certain subrectangles in the Latin squares. Subrectangles of interest can be easily identified, and in certain instances, completely enumerated. This study can be extended in several different directions, one of which is concerned with modifying the code design process in order to eliminate or reduce the number of configurations bearing a negative influence on the performance of the code. Another application of the results includes determining to which extent a configuration governs the behavior of the bit-error rate curve in the waterfall and error-floor regions     

Low-density parity-check codes based on finite geometries: arediscovery and new results

This paper presents a geometric approach to the construction of low-density parity-check (LDPC) codes. Four classes of LDPC codes are constructed based on the lines and points of Euclidean and projective geometries over finite fields. Codes of these four classes have good minimum distances and their Tanner (1981) graphs have girth 6. Finite-geometry LDPC codes can be decoded in various ways, ranging from low to high decoding complexity and from reasonably good to very good performance. They perform very well with iterative decoding. Furthermore, they can be put in either cyclic or quasi-cyclic form. Consequently, their encoding can be achieved in linear time and implemented with simple feedback shift registers. This advantage is not shared by other LDPC codes in general and is important in practice. Finite-geometry LDPC codes can be extended and shortened in various ways to obtain other good LDPC codes. Several techniques of extension and shortening are presented. Long extended finite-geometry LDPC codes have been constructed and they achieve a performance only a few tenths of a decibel away from the Shannon theoretical limit with iterative decoding     

Parity-Check Density Versus Performance of Binary Linear Block Codes: New Bounds and Applications

The moderate complexity of low-density parity-check (LDPC) codes under iterative decoding is attributed to the sparseness of their parity-check matrices. It is therefore of interest to consider how sparse parity-check matrices of binary linear block codes can be a function of the gap between their achievable rates and the channel capacity. This issue was addressed by Sason and Urbanke, and it is revisited in this paper. The remarkable performance of LDPC codes under practical and suboptimal decoding algorithms motivates the assessment of the inherent loss in performance which is attributed to the structure of the code or ensemble under maximum-likelihood (ML) decoding, and the additional loss which is imposed by the suboptimality of the decoder. These issues are addressed by obtaining upper bounds on the achievable rates of binary linear block codes, and lower bounds on the asymptotic density of their parity-check matrices as a function of the gap between their achievable rates and the channel capacity; these bounds are valid under ML decoding, and hence, they are valid for any suboptimal decoding algorithm. The new bounds improve on previously reported results by Burshtein and by Sason and Urbanke, and they hold for the case where the transmission takes place over an arbitrary memoryless binary-input output-symmetric (MBIOS) channel. The significance of these information-theoretic bounds is in assessing the tradeoff between the asymptotic performance of LDPC codes and their decoding complexity (per iteration) under message-passing decoding. They are also helpful in studying the potential achievable rates of ensembles of LDPC codes under optimal decoding; by comparing these thresholds with those calculated by the density evolution technique, one obtains a measure for the asymptotic suboptimality of iterative decoding algorithms