基于循环检测的LDPC比特翻转译码算法研究

提出一种基于循环检测的低密度奇偶校验码的比特翻转(BF)译码算法,采用对译码翻转比特的循环检测和对接受符号可靠性信息的软判决,使译码性能大大改善.理论分析表明,该译码运算复杂度低,仿真结果表明,改进的算法优于加权比特翻转译码LP-WBF算法约0.3 dB,误码性能改善明显.     

低复杂度的LDPC码联合编译码构造方法研究

LDPC码因为其具有接近香农限的译码性能和适合高速译码的并行结构,已经成为纠错编码领域的研究热点。LDPC码校验矩阵的构造是基于稀疏的随机图,所以该类码字编码和译码的硬件实现比较复杂。以单位阵的循环移位阵为基本单元,构造LDPC码的校验矩阵,降低了LDPC码在和积算法下的译码复杂度。同时考虑到LDPC码的编码复杂度,给出了一种可以简化编码的结构。针对该方案构造的LDPC码,提出了消除其二分图上的短圈的方法。通过大量的仿真和计算分析,本文比较了这种LDPC码和随机构造的LDPC码在误码率性能,圈长分布以及最小码间距估计上的差异。     

FPGA implementation of low complexity LDPC iterative decoder

Low-density parity-check (LDPC) codes, proposed by Gallager, emerged as a class of codes which can yield very good performance on the additive white Gaussian noise channel as well as on the binary symmetric channel. LDPC codes have gained lots of importance due to their capacity achieving property and excellent performance in the noisy channel. Belief propagation (BP) algorithm and its approximations, most notably min-sum, are popular iterative decoding algorithms used for LDPC and turbo codes. The trade-off between the hardware complexity and the decoding throughput is a critical factor in the implementation of the practical decoder. This article presents introduction to LDPC codes and its various decoding algorithms followed by realisation of LDPC decoder by using simplified message passing algorithm and partially parallel decoder architecture. Simplified message passing algorithm has been proposed for trade-off between low decoding complexity and decoder performance. It greatly reduces the routing and check node complexity of the decoder. Partially parallel decoder architecture possesses high speed and reduced complexity. The improved design of the decoder possesses a maximum symbol throughput of 92.95 Mbps and a maximum of 18 decoding iterations. The article presents implementation of 9216 bits, rate-1/2, (3, 6) LDPC decoder on Xilinx XC3D3400A device from Spartan-3A DSP family.     

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     

On decoding of low-density parity-check codes over the binary erasure channel

This paper investigates decoding of low-density parity-check (LDPC) codes over the binary erasure channel (BEC). We study the iterative and maximum-likelihood (ML) decoding of LDPC codes on this channel. We derive bounds on the ML decoding of LDPC codes on the BEC. We then present an improved decoding algorithm. The proposed algorithm has almost the same complexity as the standard iterative decoding. However, it has better performance. Simulations show that we can decrease the error rate by several orders of magnitude using the proposed algorithm. We also provide some graph-theoretic properties of different decoding algorithms of LDPC codes over the BEC which we think are useful to better understand the LDPC decoding methods, in particular, for finite-length codes.     

Novel modified min-sum decoding algorithm for low-density parity-check codes

The problem of improving the performance of min-sum decoding of low-density parity-check(LDPC)codes is considered in this paper.Based on rain-sum algorithm,a novel modified min-sum decoding algorithm for LDPC codes is proposed.The proposed algorithm modifies the variable node message in the iteration process by averaging the new message and previous message if their signs are different.Compared with the standard min-sum algorithm,the modification is achieved with only a small increase in complexity,but significantly improves decoding performance for both regular and irregular LDPC codes.Simulation results show that the performance of our modified decoding algorithm is very close to that of the standard sum-produet algorithm for moderate length LDPC codes.     

Design of serial concatenated MSK schemes based on density evolution

We consider the design of convolutional codes and low density parity check (LDPC) codes with minimum-shift keying (MSK) when the receiver employs iterative decoding and demodulation. The main idea proposed is the design of coded schemes that are well matched to the iterative decoding algorithm being used rather than to hypothetical maximum-likelihood decoding. We first show that the design is crucially dependent on whether the continuous phase encoder (CPE) is realized in recursive form or in nonrecursive form. We then consider the design of convolutionally coded systems and low density parity check codes with MSK to obtain near-capacity performance. With convolutional codes, we show that it is possible to improve the performance significantly by using a mixture of recursive and nonrecursive realizations for the CPE. For low density parity check codes, we show that codes designed for binary phase shift keying are optimal for MSK only if the nonrecursive realization is used; for the recursive realization, we design new LDPC codes based on the concept of density evolution. We show that these codes outperform the best known codes for MSK and have lower decoding complexity.     

Implementation aspects of LDPC convolutional codes

Potentially large storage requirements and long initial decoding delays are two practical issues related to the decoding of low-density parity-check (LDPC) convolutional codes using a continuous pipeline decoder architecture. In this paper, we propose several reduced complexity decoding strategies to lessen the storage requirements and the initial decoding delay without significant loss in performance. We also provide bit error rate comparisons of LDPC block and LDPC convolutional codes under equal processor (hardware) complexity and equal decoding delay assumptions. A partial syndrome encoder realization for LDPC convolutional codes is also proposed and analyzed. We construct terminated LDPC convolutional codes that are suitable for block transmission over a wide range of frame lengths. Simulation results show that, for terminated LDPC convolutional codes of sufficiently large memory, performance can be improved by increasing the density of the syndrome former matrix.     

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     

基于原型图的低码率LDPC码最小和译码算法改进方案

低密度奇偶校验(LDPC)码的译码硬件实现方案大多采用计算复杂度较低的修正最小和(NMS)算法,然而对于低码率LDPC码,由于校验节点度数低,NMS算法的修正误差较大,导致其译码性能和标准的置信传播(BP)算法相比有较大差异。该文针对基于原图构造的一类低码率LDPC码,提出了在NMS迭代译码中结合震荡抵消(OSC)处理和多系数(MF)修正技术的方案。结合低码率原型图LDPC码行重分布差异较大的特点,MF修正算法可以有效地减少计算误差,从而改善译码性能。另外低码率原型图LDPC码的收敛较慢,而OSC处理则可以较好地抑制正反馈信息,进一步提高NMS算法的性能增益。仿真结果表明,对于此类低码率LDPC码, MF-OSC-NMS算法可以达到接近BP算法的性能。OSC处理和MF修正技术硬件实现简单,与NMS算法相比几乎没有增加计算复杂度,因此MF-OSC-NMS算法是译码算法复杂度和性能之间一个较好的折中处理方案。     

High-performance simple-encoding generator-based systematic irregular LDPC codes and resulted product codes

Low-Density Parity-Check (LDPC) code is one of the most exciting topics among the coding theory community.It is of great importance in both theory and practical communications over noisy channels.The most advantage of LDPC codes is their relatively lower decoding complexity compared with turbo codes,while the disadvantage is its higher encoding complexity.In this paper,a new ap- proach is first proposed to construct high performance irregular systematic LDPC codes based on sparse generator matrix,which can significantly reduce the encoding complexity under the same de- coding complexity as that of regular or irregular LDPC codes defined by traditional sparse parity-check matrix.Then,the proposed generator-based systematic irregular LDPC codes are adopted as con- stituent block codes in rows and columns to design a new kind of product codes family,which also can be interpreted as irregular LDPC codes characterized by graph and thus decoded iteratively.Finally, the performance of the generator-based LDPC codes and the resultant product codes is investigated over an Additive White Gaussian Noise (AWGN) and also compared with the conventional LDPC codes under the same conditions of decoding complexity and channel noise.     

低密度校验码及其在图像传输中的应用

低密度校验(Low-Density Parity-Check,LDPC)码是一种基于图和迭代译码的信道编码方案,性能非常接近Shannon极限且实现复杂度低,具有很强的纠错抗干扰能力。该文深入研究了LDPC码的编码和译码基本原理,并将其应用于移动衰落信道图像的传输中,仿真结果表明LDPC码能为图像传输带来显著的性能提高,且系统复杂度低,译码时延短。     

Reduced-Complexity Decoding of LDPC Codes

Reduced-Complexity Decoding of LDPC Codes Various log-likelihood-ratio-based belief-propagation (LLR- BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representation of the decoding computations are shown to achieve a reduction in complexity, by simplifying the check-node update or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from a performance, latency, computational complexity, and memory-requirement perspective.     

Iterative reliability-based decoding of low-density parity checkcodes

In this paper, reliability based decoding is combined with belief propagation (BP) decoding for low-density parity check (LDPC) codes. At each iteration, the soft output values delivered by the BP algorithm are used as reliability values to perform reduced complexity soft decision decoding of the code considered. This approach allows to bridge the error performance gap between belief propagation decoding which remains suboptimum, and maximum likelihood decoding which is too complex to be implemented for the codes considered. Trade-offs between decoding complexity and error performance are also investigated. In particular, a stopping criterion which reduces the average number of iterations at the expense of very little performance degradation is proposed for this combined decoding approach. Simulation results for several Gallager (1963, 1968) LDPC codes and different set cyclic codes of hundreds of information bits are given and elaborated     

改进的离散字母表迭代译码算法研究

为了优化LDPC迭代译码性能和降低算法复杂度,提出了一种改进的基于Gallager A算法的2b离散字母表迭代译码算法。在每一轮迭代中,Tanner图上的校验节点与变量节点之间所传递的消息有1b表示符号值,另1b反映码字结构特性,其中变量节点更新规则是通过查表法来实现的。在二元对称信道下针对列重为3的规则LDPC码做了仿真实验,仿真结果表明该算法性能明显优于原算法,并且具有较低的复杂度。     

基于SISO的LDPC码盲译码算法研究

针对传统BP译码算法需要初始条件的缺点,本文提出了一种基于软输入软输出(SISO)的LDPC码盲译码算法,所提算法采用类似BP迭代译码算法步骤,通过对距离信息进行迭代处理,实现无需接收信号的信噪比和信道状态即可译码;同时,还将所提盲译码算法推广到多进制LDPC码的译码应用中。本文所提盲译码算法在初始状态难以确定以及接收信号信噪比难以估计的通信信道中具有重要价值。仿真结果表明,所提算法不论是在AWGN信道还是在瑞利衰落信道上都能取得优良的性能,不论是与标准BP译码算法还是与分层BP译码算法相比,在性能相近的情况下,计算复杂度都有所降低。     

一种低译码复杂度的Turbo架构LDPC码

针对低密度奇偶校验(LDPC)码较大的译码复杂度和RAM占用,该文提出了一种低译码复杂度的Turbo架构LDPC码并行交织级联Gallager码 (Parallel Interleaved Concatenated Gallager Code,PICGC)。该文给出了PICGC的设计方法和编译码算法,并分析比较了PICGC译码器与LDPC译码器所需的RAM存储量,推导出RAM节省比的上界。理论分析和仿真结果表明,PICGC以纠错性能略微降低为代价,有效地降低译码复杂度和RAM存储量,且译码时延并未增加,是一种有效且易于实现的信道编码方案。     

低密度奇偶校验码构造及编译码研究进展

低密度奇偶校验(LDPC)码具有接近Shannon限的良好性能,能有效提高数据传输的可靠性.为提高LDPC码的性能,对码字的研究多集中于构造、编码和译码这几方面的基础研究.首先简要给出了LDPC码的基本描述,然后对二进制和多进制LDPC码的关键技术进行了系统归纳和全新分类,分别从构造、编码和译码3个方面进行了详细探讨,重点对最新的研究成果进行了全面分析和总结,对LDPC码今后的研究具有指导意义.     

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。