非正则LDPC码在DSP上的实现

首先简单介绍了非正则LDPC码的结构,给出了一种基于IEEE802.16e直接编码法生成的(576,288)非正则LDPC码的编译码原理。然后详细论述了其在TI定点DSP(TMS320C5510)上的定点化算法实现方式,并在经过C和部分汇编优化后将算法效率提高了70%以上,达到了实时系统要求。最后给出了该LDPC码与(2,1,7)卷积码在AWGN信道下的性能对比,表明这种中短码长的非正则LDPC码较卷积码有较大的纠错性能优势。     

LDPC码的分析和非正则图的设计

给出了正则LDPC码和非正则LDPC码的分析，并介绍了非正则LDPC码的设计方法。     

非正则LDPC码在AWGN信道中的性能研究

简单介绍了非正则低密度奇偶校验(Low Density Parity Check,LDPC)码的结构。研究了其对数域概率译码的和积算法(Sum Product Algorithm,SPA),并对该算法的主要公式进行了推导,给出了其迭代核心部分的C语言实现伪码。对一种基于802.16e直接编码法生成的非正则LDPC码在高斯信道下进行了仿真分析,表明中短码长的非正则LDPC码已经具有优异的纠错性能。     

Rice信道下LDPC码密度进化的研究

应用低密度奇偶校验(LDPC)码译码消息的密度进化可以得到码集的噪声门限,依此评价不同译码算法的性能,并可以用来优化非正则LDPC码的次数分布对。该文首先以Rice信道下正则LDPC码为例,讨论了不同量化阶数及步长时BP,BP-based 和offset BP-based 3种译码算法的DDE(Discrete Density Evolution)分析,接着在offset BP-based译码算法的DDE分析基础上,采用差分进化方法对Rice信道下非正则LDPC码的次数分布对进行了优化,得出了相应的噪声门限。最后,给出了Rice信道下码率为1/2的优化非正则LDPC码的概率聚集函数(PMF)进化曲线。     

一种具有较大围长的正则LDPC码构造方法

提出一种新的具有较大围长的正则LDPC码构造方法。首先介绍以矩阵分裂技术为基础的高围长正则LDPC码的构造方法,并在此基础上分析了设计围长时参数的选取方法。仿真表明,用这种方法构造的正则LDPC码围长可以达到12,并且在AWGN信道下的性能不差于相同参数、随机构造的LDPC码,在高信噪比时甚至优于相同参数的随机码。     

非规则LDPC码简化译码算法研究

主要介绍了低密度奇偶校验码(LDPC)的基本概念及规则和非规则两种典型的LDPC码的结构,简要介绍了LDPC码的译码算法,在此基础上,引出两种非规则LDPC码的简化译码算法,仿真验证算法较大地降低了译码复杂度,并在高信噪比下性能损失较小.     

一种半随机半代数的非正则LDPC码构造方法

LDPC码由于其卓越的纠错性能引起了学术界的广泛重视，当前LDPC所面临的一个主要问题是其编码复杂性的问题。本文给出了一种半代数半随机的非正则LDPC码构造方法，由该方法所构造的校验矩阵具有近似下三角特性，从而可以大大降低LDPC的编译码复杂性，同时具有与完全随机LDPC码相匹配的性能。     

LDPC码在非相干BFSK系统中的性能分析

通过理论分析和计算机仿真研究了LDPC码在非相干BFSK系统中的性能.首先证明单输入双输出的非相干BFSK系统满足信道对称条件,然后分别利用离散密度进化(DDE)和高斯近似(GA)算法给出了LDPC码的性能下界,后者与前者相比性能非常接近而且计算量要小很多.通过仿真得到几种常用LDPC码的性能并与译码门限以及香农极限进...     

LDPC码在无线图像传输中的应用

介绍了LDPC码编、译码基本原理。在此基础上介绍了几种LDPC码在无线图像传输中典型应用方案,这些方案都结合了LDPC码的特性及JPEG2000的码流特点,较大提高了无线图像传输的可靠性及有效性。     

一类基于填充设计的LDPC码的构造

文中利用填充设计,构造了一类具有特定结构的任意码长的LDPC(低密度校验)码,这类LDPC码不仅具有好的最小距离,其Tanner图的最小周长为6,而且它的编码和译码结构可通过移位寄存器线性产生,其性能优于随机产生的LD-PC码。     

On the application of LDPC codes to arbitrary discrete-memoryless channels

We discuss three structures of modified low-density parity-check (LDPC) code ensembles designed for transmission over arbitrary discrete memoryless channels. The first structure is based on the well-known binary LDPC codes following constructions proposed by Gallager and McEliece, the second is based on LDPC codes of arbitrary (q-ary) alphabets employing modulo-q addition, as presented by Gallager, and the third is based on LDPC codes defined over the field GF(q). All structures are obtained by applying a quantization mapping on a coset LDPC ensemble. We present tools for the analysis of nonbinary codes and show that all configurations, under maximum-likelihood (ML) decoding, are capable of reliable communication at rates arbitrarily close to the capacity of any discrete memoryless channel. We discuss practical iterative decoding of our structures and present simulation results for the additive white Gaussian noise (AWGN) channel confirming the effectiveness of the codes.     

Pseudo-Codeword Analysis of Tanner Graphs From Projective and Euclidean Planes

We consider coded data transmission over a binary-input output-symmetric memoryless channel using a binary linear code. In order to understand the performance of maximum-likelihood (ML) decoding, one studies the codewords, in particular the minimal codewords, and their Hamming weights. In the context of linear programming (LP) decoding, one's attention needs to be shifted to the pseudo-codewords, in particular, to the minimal pseudo-codewords and their pseudo-weights. In this paper, we investigate some families of codes that have good properties under LP decoding, namely certain families of low-density parity-check (LDPC) codes that are derived from projective and Euclidean planes: we study the structure of their minimal pseudo-codewords and give lower bounds on their pseudo-weight. Besides this main focus, we also present some results that hold for pseudo-codewords and minimal pseudo-codewords of any Tanner graph, and we highlight how the importance of minimal pseudo-codewords under LP decoding varies depending on which binary-input output-symmetric memoryless channel is used.     

Hybrid Hard-Decision Iterative Decoding of Irregular Low-Density Parity-Check Codes

Time-invariant hybrid (Hscr_TI) decoding of irregular low-density parity-check (LDPC) codes is studied. Focusing on Hscr_TI algorithms with majority-based (MB) binary message-passing constituents, we use density evolution (DE) and finite-length simulation to analyze the performance and the convergence properties of these algorithms over (memoryless) binary symmetric channels. To apply DE, we generalize degree distributions to have the irregularity of both the code and the decoding algorithm embedded in them. A tight upper bound on the threshold of MB Hscr_TI algorithms is derived, and it is proven that the asymptotic error probability for these algorithms tends to zero, at least exponentially, with the number of iterations. We devise optimal MB Hscr_TI algorithms for irregular LDPC codes, and show that these algorithms outperform Gallager's algorithm A applied to optimized irregular LDPC codes. We also show that compared to switch-type algorithms, such as Gallager's algorithm B, where a comparable improvement is obtained by switching between different MB algorithms, MB Hscr_TI algorithms are more robust and can better cope with unknown channel conditions, and thus can be practically more attractive     

不同译码下LDPC码的图像应用

文中介绍了LDPC码的构造及其译码原理,该码在中短码长时具有很强的纠错能力,将其与图像传榆相结合,能够达到很好的图像传输质量。仿真实现了在AWGN信道下规则LDPC码的图像传输系统,用四种译码算法对LDPC码的性能做了仿真,并将其在图像传输中的效果做了比较。仿真结果发现,LDPC码采用不同的译码算法输出图像的效果是不一样的。     

瑞利衰落信道下基于LDPC码USTM的MIMO系统

研究了一种联合低密度校验(LDPC,Low-Density Parity-Check)码和酉空时调制(USTM,Unitary Space-Time Modulation)技术在不相关瑞利平坦衰落(Rayleigh flat fading)下的多输入多输出信道(MIMO,Multiple-Input Multiple-Output)系统的性能.在无信道状态信息下,采用可并行操作的和积译码算法(SPA,Sum-Product Algorithm)的LDPCC-USTM级联系统具有优异的性能,并分析了不同LDPC码集下对系统性能的影响.仿真结果表明LDPCC-USTM级联系统比与未级联的相比有近23dB的编码增益,与基于Turbo码的USTM[6]系统相比有5dB多的编码增益,且基于非规则的LDPC码的级联系统比基于规则码有近1dB的编码增益.     

On Universal Properties of Capacity-Approaching LDPC Code Ensembles

This paper is focused on the derivation of some universal properties of capacity-approaching low-density parity-check (LDPC) code ensembles whose transmission takes place over memoryless binary-input output-symmetric (MBIOS) channels. Properties of the degree distributions, graphical complexity, and the number of fundamental cycles in the bipartite graphs are considered via the derivation of information-theoretic bounds. These bounds are expressed in terms of the target block/bit error probability and the gap (in rate) to capacity. Most of the bounds are general for any decoding algorithm, and some others are proved under belief propagation (BP) decoding. Proving these bounds under a certain decoding algorithm, validates them automatically also under any suboptimal decoding algorithm. A proper modification of these bounds makes them universal for the set of all MBIOS channels which exhibit a given capacity. Bounds on the degree distributions and graphical complexity apply to finite-length LDPC codes and to the asymptotic case of an infinite block length. The bounds are compared with capacity-approaching LDPC code ensembles under BP decoding, and they are shown to be informative and are easy to calculate. Finally, some interesting open problems are considered.     

Asymptotic enumeration methods for analyzing LDPC codes

We show how asymptotic estimates of powers of polynomials with nonnegative coefficients can be used in the analysis of low-density parity-check (LDPC) codes. In particular, we show how these estimates can be used to derive the asymptotic distance spectrum of both regular and irregular LDPC code ensembles. We then consider the binary erasure channel (BEC). Using these estimates we derive lower bounds on the error exponent, under iterative decoding, of LDPC codes used over the BEC. Both regular and irregular code structures are considered. These bounds are compared to the corresponding bounds when optimal (maximum-likelihood (ML)) decoding is applied.     

Performance analysis and code optimization of low densityparity-check codes on Rayleigh fading channels

A numerical method has been presented to determine the noise thresholds of low density parity-check (LDPC) codes that employ the message passing decoding algorithm on the additive white Gaussian noise (AWGN) channel. In this paper, we apply the technique to the uncorrelated flat Rayleigh fading channel. Using a nonlinear code optimization technique, we optimize irregular LDPC codes for such a channel. The thresholds of the optimized irregular LDPC codes are very close to the Shannon limit for this channel. For example, at rate one-half, the optimized irregular LDPC code has a threshold only 0.07 dB away from the capacity of the channel. Furthermore, we compare simulated performance of the optimized irregular LDPC codes and turbo codes on a land mobile channel, and the results indicate that at a block size of 3072, irregular LDPC codes can outperform turbo codes over a wide range of mobile speeds     

状态网格图的LDPC码在OFDM系统中的应用

介绍了LDPC码的编译码技术,提出了一种新颖的2状态网格图译码算法,研究了该码在OFDM系统中的性能,对不同的译码算法进行了比较.仿真结果表明,LDPC码在OFDM基带传输系统中用2状态网格图对其译码能够更好的对错误码进行纠错,提高码字性能,信息传输速率会大大提高.     

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