RS码短码的软判决方法

RS码是差错控制领域中一种重要的线性分组码,而相对于普通的硬判决来说,软判决译码具有纠错能力强的优势。针对RS码(15,11),提出了使用Chase算法实现非二进制码的软判决译码方法,并通过计算机模拟,给出在AWGN信道中的译码结果。     

Turbo乘积码梯度译码算法研究

Turbo乘积码(简称TPC码)是一类采用简单的行列交织器将分组码进行串行级联而构成的纠错码.文中针对二进制turbo乘积码提出了一种快速的软判决译码算法一梯度译码算法.该算法是以迭代Chase算法为基础,通过利用chase算法上次迭代译码而得到的每行(或列)最优判决码D(m-1)来代替竞争码字C,节省了寻找C的过程,从而简化了外信息和软输出的计算.仿真结果表明:梯度算法能在基本保持turbo乘积码的Chase算法译码性能基础上,提高了译码速度,降低了译码复杂度.     

RS码软判决译码算法研究的最新进展

本文在简要介绍RS码的基本概念及其译码算法的基础上，着重介绍了近几年来RS码软判决译码算法的最新进展，其中包括最大似然译码、代数软判决译码、基于Turbo编译码的软判决译码以及基于和积算法(SPA)的软判决译码算法等。     

一种应用于DMB-T的基于RS码软判决译码的级联码方案

结合DMB—T系统的信道编码方案以及编码领域的最新进展,即RS码软判决译码算法的发展,提出了一种应用于DMB-T系统基于和积算法(SPA)的RS码软判决译码的级联码方案。该方案仅需在现有方案的基础上作少量调整即可获得可观的编码增益。和其它流行的软判决译码算法的比较表明,此方案提出的RS码软判决译码算法在DMB-T系统的应用中具有一定的优势。     

Turbo乘积码译码算法的简化

Chase算法是Turbo乘积码(TPC)软判决译码中常采用的算法之一。分析了传统Chase算法中寻找竞争码字对译码复杂度的影响,在此基础上提出了两种新的简化译码算法,省去了寻找竞争码字的过程。仿真结果表明,简化算法在基本保持传统Chase算法译码性能的基础上,降低了译码复杂度,提高了译码速度。     

一种基于Chase的RS码代数软判决译码算法

为了提高RS码的纠错性能,本文提出了一种基于Chase的代数软判决译码算法,称为Chase-ASD.该算法充分利用了接收比特的可信度信息,但运算复杂度较高.针对该算法运算复杂度高的问题,本文进一步给出了简化的Chase-ASD算法.仿真结果表明,提出的Chase-ASD和简化的Chase-ASD算法均可比原ASD算法提供更多的译码增益.     

RS码译码算法对比研究

RS码所具有的高效译码性能使其被广泛应用于数据通信和存储系统的差错控制中。本文主要对目前常用的RS码的硬判决译码算法和K—V代数软判决译码算法进行对比研究。通过对两种算法原理的理论分析,给出了RS码在硬判决与软判决的算法下的计算机仿真。结果表明两种算法均能得到良好的译码效果,而软判决译码算法较硬判决方式能更有效地带来系统增益。而软判决译码算法可以通过适当提高复杂度来改善系统的性能。     

一种新的缩短RS码迭代译码方案

为了在EPON中应用GF(256)标准RS码对信息帧长大于255位的信息流进行编码,并提高RS码的编码增益,提出了一种新的缩短RS码的编译码方案。该方案通过两个缩短RS码的交叠编码和互相迭代译码,可以提高编译码增益。RS码BM硬判决译码和chase软判决译码的计算机仿真表明,该方案对缩短RS码的软硬判决译码性能都有明显提高。     

软信息辅助硬判决译码在自由空间光通信中的应用

由于在性能和复杂度之间的良好折中,软信息辅助硬判决译码近年来受到了光通信领域的高度关注,其中包括了软信息辅助比特标记(Soft-Aided Bit-Marking,SABM)算法.为了易于硬件实现,本文基于阶梯码(Staircase Code,SCC)提出了一种改进型SABM算法(improved SABM,iSABM),称为iSABM-SCC.iSABM-SCC利用信道软信息,通过两个可信度阈值将硬判决输出比特标记为三种可信度等级,用以辅助硬判决译码识别译码错误和扩展纠错能力,达到提升阶梯码性能的目的.在受大气湍流影响的自由空间光通信中的仿真表明,iSABM-SCC性能显著优于标准SCC和RS码.以强湍流信道为例,码率为0.75的iSABM-SCC在4-PAM调制下较标准SCC产生的性能增益可达4.37 dB,在8-PAM调制下较RS码产生的性能增益可达11.06 dB.     

Reed-Solomon码性能增强译码算法

Reed-Solomon (RS)码具有优良的纠随机错误和突发错误的能力,被广泛应用于通信系统中。依据RS码定义的计算映射与按BCH子码编码2种不同的编码方式,对RS码性能增强算法进行了分类总结,并对比了这2类性能增强算法的异同。仿真结果表明,相对于传统的BMA译码算法,在误帧率为10-3时,KV算法可以提升RS码0.35 d B的性能增益,Chase译码算法有大约0.65 d B的增益,指出了RS码性能增强算法中Chase列表译码算法的后续研究方向。     

Iterative algebraic soft-decision list decoding of Reed-Solomon codes

In this paper, we present an iterative soft-decision decoding algorithm for Reed-Solomon (RS) codes offering both complexity and performance advantages over previously known decoding algorithms. Our algorithm is a list decoding algorithm which combines two powerful soft-decision decoding techniques which were previously regarded in the literature as competitive, namely, the Koetter-Vardy algebraic soft-decision decoding algorithm and belief-propagation based on adaptive parity-check matrices, recently proposed by Jiang and Narayanan. Building on the Jiang-Narayanan algorithm, we present a belief-propagation-based algorithm with a significant reduction in computational complexity. We introduce the concept of using a belief-propagation-based decoder to enhance the soft-input information prior to decoding with an algebraic soft-decision decoder. Our algorithm can also be viewed as an interpolation multiplicity assignment scheme for algebraic soft-decision decoding of RS codes.     

Applications of algebraic soft-decision decoding of Reed-Solomon codes

Efficient soft-decision decoding of Reed-Solomon (RS) codes is made possible by the Koetter-Vardy (KV) algorithm which consists of a front-end to the interpolation-based Guruswami-Sudan (GS) list decoding algorithm. This paper approaches the soft-decision KV algorithm from the point of view of a communications systems designer who wants to know what benefits the algorithm can give, and how the extra complexity introduced by soft decoding can be managed at the systems level. We show how to reduce the computational complexity and memory requirements of the soft-decision front-end. Applications to wireless communications over Rayleigh fading channels and magnetic recording channels are proposed. For a high-rate RS(255,239) code, 2-3 dB of soft-decision gain is possible over a Rayleigh fading channel using 16-quadrature amplitude modulation. For shorter codes and at lower rates, the gain can be as large as 9 dB. To lower the complexity of decoding on the systems level, the redecoding architecture is explored, which uses only the appropriate amount of complexity to decode each packet. An error-detection criterion based on the properties of the KV decoder is proposed for the redecoding architecture. Queueing analysis verifies the practicality of the redecoding architecture by showing that only a modestly sized RAM buffer is required.     

Reduced Complexity Interpolation Architecture for Soft-Decision Reed–Solomon Decoding

Reed-Solomon (RS) codes are one of the most widely utilized block error-correcting codes in modern communication and computer systems. Compared to hard-decision decoding, soft-decision decoding offers considerably higher error-correcting capability. The Koetter-Vardy (KV) soft-decision decoding algorithm can achieve substantial coding gain, while maintaining a complexity polynomial with respect to the code word length. In the KV algorithm, the interpolation step dominates the decoding complexity. A reduced complexity interpolation architecture is proposed in this paper by eliminating the polynomial updating corresponding to zero discrepancy coefficients in this step. Using this architecture, an area reduction of 27% can be achieved over prior efforts for the interpolation step of a typical (255, 239) RS code, while the interpolation latency remains the same     

Low-Latency Factorization Architecture for Algebraic Soft-Decision Decoding of Reed–Solomon Codes

Bivariate polynomial factorization is an important stage of algebraic soft-decision decoding of Reed-Solomon (RS) codes and contributes to a significant portion of the overall decoding latency. With the exhaustive search-based root computation method, factorization latency is dominated by the root computation step, especially for RS codes defined over very large finite fields. The root-order prediction method proposed by Zhang and Parhi only improves average latency, but does not have any effect on the worst-case latency of the factorization procedure. Thus, neither approach is well-suited for delay-sensitive applications. In this paper, a novel architecture based on direct root computation is proposed to greatly reduce the factorization latency. Direct root computation is feasible because in most practical applications of algebraic soft-decision decoding of RS codes, enough decoding gain can be achieved with a relatively low interpolation cost, which results in a bivariate polynomial with low Y-degree. Compared with existing works, not only does the new architecture have a significantly smaller worst-case decoding latency, but it is also more area efficient since the corresponding hardware for routing polynomial coefficients is eliminated.     

Fast factorization architecture in soft-decision Reed-Solomon decoding

Reed-Solomon (RS) codes are among the most widely utilized block error-correcting codes in modern communication and computer systems. Compared to its hard-decision counterpart, soft-decision decoding offers considerably higher error-correcting capability. The recent development of soft-decision RS decoding algorithms makes their hardware implementations feasible. Among these algorithms, the Koetter-Vardy (KV) algorithm can achieve substantial coding gain for high-rate RS codes, while maintaining a polynomial complexity with respect to the code length. In the KV algorithm, the factorization step can consume a major part of the decoding latency. A novel architecture based on root-order prediction is proposed in this paper to speed up the factorization step. As a result, the time-consuming exhaustive-search-based root computation in each iteration level, except the first one, of the factorization step is circumvented with more than 99% probability. Using the proposed architecture, a speedup of 141% can be achieved over prior efforts for a (255, 239) RS code, while the area consumption is reduced to 31.4%.     

High-Speed Interpolation Architecture for Soft-Decision Decoding of Reed–Solomon Codes

Algebraic soft-decision decoding of Reed-Solomon (RS) codes delivers promising coding gains over conventional hard-decision decoding. The most computationally demanding step in soft-decision decoding of RS codes is bivariate polynomial interpolation. In this paper, we present a hybrid data format-based interpolation architecture that is well suited for high-speed implementation of the soft-decision decoders. It will be shown that this architecture is highly scalable and can be extensively pipelined. It also enables maximum overlap in time for computations at adjacent iterations. It is estimated that the proposed architecture can achieve significantly higher throughput than conventional designs with equivalent or lower hardware complexity     

Applications of Algebraic Soft-Decision Decoding of Reed–Solomon Codes

Efficient soft-decision decoding of Reed–Solomon codes is made possible by the Koetter–Vardy (KV) algorithm which consists of a front-end to the interpolation-based Guruswami–Sudan list decoding algorithm. This paper approaches the soft-decision KV algorithm from the point of view of a communications systems designer who wants to know what benefits the algorithm can give, and how the extra complexity introduced by soft decoding can be managed at the systems level. We show how to reduce the computational complexity and memory requirements of the soft-decision front-end. Applications to wireless communications over Rayleigh fading channels and magnetic recording channels are proposed. For a high-rate (RS 9225,239) Reed–Solomon code, 2–3 dB of soft-decision gain is possible over a Rayleigh fading channel using 16-quadrature amplitude modulation. For shorter codes and at lower rates, the gain can be as large as 9 dB. To lower the complexity of decoding on the systems level, the redecoding architecture is explored which uses only the appropriate amount of complexity to decode each packet. An error-detection criterion based on the properties of the KV decoder is proposed for the redecoding architecture. Queuing analysis verifies the practicality of the redecoding architecture by showing that only a modestly sized RAM buffer is required.     

Split soft-decision equalization for wireless channels with large delay spread

A novel split soft-decision equalizer (SSE) with near-optimum performance is proposed for wireless multipath channels with large delay spread. The concept of SSE is significantly different from the traditional notions of the Viterbi algorithm and decision-feedback equalizer. Instead of dealing with received sequence as a combined sequence, it splits the received sequence into its constituent paths. Using an iterative soft-decision algorithm, reliability of the soft decisions on each decomposed element is improved iteratively. The major advantage of SSE is the independence of computation complexity on channel time dispersion. Joint design of SSE with a soft-decision decoder is also considered in this paper. Performance analysis and simulation results show that performance of the proposed algorithm comes very close to that of the logarithmic maximum a posteriori decoder.