基于高斯消元的BATS码的改进译码算法

BATS码是一种包括外码和内码的纠删码,外码是喷泉码的矩阵形式。内码是网络编码结构,采用随机线性网络编码算法。BATS码的常用译码算法为BP译码算法,但对有限长BATS码,BP译码算法的性能有大幅度的衰减。因此,在计算资源充足的情况下,可以在BP译码器后采用高斯消元算法对BATS码进行译码。采用高斯消元算法时,矩阵满秩则输入数据包可译,反之则不可译。因此,利用当矩阵不满秩时也有部分包可译的特点,识别并将这部分可译包译出,从而提高高斯消元译码器的译码性能。     

残差扰动网络辅助的BP译码算法

极化码的置信传播(Belief Propagation,BP)译码算法性能相比于其他极化码译码算法并不具有优势。为了改善这一现象,提出了一种基于残差网络和扰动译码算法相结合的BP译码算法。该算法通过在传统BP译码算法的基础上添加残差神经网络对接收信号进行处理,使其更大概率地落在可正确译码区域内,从而达到改善传统BP译码算法的译码性能的目的。仿真结果表明,在误比特率为10－4时,所提算法相比于传统的BP译码算法约有0.7 dB的性能增益,相比于BP-RNND(50)（BP- Residual Neural Network Decoder）译码算法约有0.6 dB的性能增益;同时,在低信噪比时所提算法的平均迭代次数相比于传统BP译码算法约有60%的降低。     

多元LDPC码串行译码算法在光纤通信中的应用

将串行BP译码算法用在多元LDPC码中,降低了在光纤传输系统中的译码延时.详细介绍了在多元LDPC码中的串行BP译码算法和光纤通信系统的仿真模型.给出了在采用串行BP算法的LDPC译码器中,译码最大迭代数量对译码性能的影响,比较了采用传统的BP算法扣串行BP算法时LDPC译码器的性能.结果表明,采用串行BP算法确实能够提升LDPC译码器的收敛速度.     

系统RA码的基于WBF策略的改进BP译码算法

 针对重复累积(RA)码译码算法(BP算法和最小和算法)复杂度高或纠错性能下降的问题,将加权位翻转WBF的思想用于改进BP算法,提出了基于WBF策略的改进BP译码算法. 在每次迭代译码中若未能译出合法码字,则按一定规则进行位翻转操作,以期获得合法码字. 仿真结果表明,本算法能有效降低系统RA码的运算复杂度,且能保持优异的译码性能.     

IEEE802.16e标准下LDPC码译码性能分析

LDPC码是一种逼近香农极限的线性分组码。对IEEE802.16e标准下LDPC码校验矩阵的构造及硬判决算法、BP算法、Log-BP算法和Min-Sum算法原理进行了简单介绍。基于Matlab仿真对不同码长译码性能及不同方法译码性能进行分析比较,结果显示随着码长的增加LDPC译码性能变好,讨论的几种译码算法中硬判决译码性能较差,Min-Sum译码好于硬判决译码,但稍差于BP译码和Log-BP译码,BP译码和Log-BP译码性能较好且基本一致。     

LDPC码的译码算法

介绍了LDPC(低密度奇偶校验码)码的BP算法和基于BP的简化译码算法，并在AWGN(加性白高斯噪声)环境下进行了各自的仿真。通过误码性能和译码复杂度两方面的比较表明BP算法的性能更优越，但简化算法的复杂度相对来说有大幅的下降。     

数字电视传输网络中的LDPC码译码算法研究

对目前应用在数字电视传输网络中的LDPC码译码算法进行归纳与总结,分别应用对数似然比测度和积译码算法及其简化算法在高斯信道上仿真,通过误码性能和译码复杂度两方面的比较表明对数似然比测度和积译码算法性能更优越,但最小和算法的复杂度相对来说有大幅下降.     

一种简化的LDPC码BP译码算法的研究

低密度奇偶校验码(LDPC码)是一种逼近香农限的线性分组码,译码的复杂度较低;在LDPC码译码算法中性能较好的是置信传播译码(BP)算法,他能够在迭代译码过程中确定码字是否已译出,但是复杂度高,运算量大。采用一种改进的BP译码算法,在迭代译码过程中对校验节点的更新信息进行曲线拟合,以减小译码运算量,有利于硬件的并行实现,减少译码延时。仿真结果表明,改进的BP算法译码性能和原来的BP算法接近,而且复杂度较低。     

一种改进的无线光通信LDPC码译码算法

针对无线光通信中低密度奇偶校验码(LDPC)置信传播(BP)译码算法复杂度高及置信度振荡造成译码错误等缺点,基于对数BP算法提出了一种改进的译码算法。改进的译码算法在校验节点运算时,判断输入到校验节点消息的最小值与某个门限的大小,根据比较结果,分别用消息最小值或若干个最小值进行运算,在损失很少性能的情况下降低了运算复杂度;同时在比特节点采用振荡抵消处理运算,提高了算法的性能增益。最后在对数正态分布湍流信道模型下,分别对比特充分交织和交织深度为16的情况进行了仿真实验。仿真结果表明,改进的译码算法与BP算法相比,大幅度降低了计算复杂度,而且译码性能有一定的优势,收敛速度损失很少;而相对于最小和算法,改进的算法虽然译码复杂度有所增加,但误码率性能有明显的优势,并且收敛速度也优于最小和算法。因此,改进的译码算法是无线光通信中LDPC码译码算法复杂度和性能之间一个较好的折中处理方案。     

LDPC译码方法研究及实现

本文主要研究了低密度校验码（LDPC码）的编译码方法及其硬件实现。在讨论几种主要的LDPC码的编译码方法的基础上，对LDPC译码错误产生原因进行了分析，提出了一种改进的置信传播译码算法——最小和算法，该算法在几乎没有增加运算复杂度的情况下，明显地提高了译码性能。同时，本文基于几何思想的LDPC码为例，提出了并串结合的FPGA实现方法，给出了仿真结果。     

Soft-bit decoding of regular low-density parity-check codes

A novel representation, using soft-bit messages, of the belief propagation (BP) decoding algorithm for low-density parity-check codes is derived as an alternative to the log-likelihood-ratio (LLR)-based BP and min-sum decoding algorithms. A simple approximation is also presented. Simulation results demonstrate the functionality of the soft-bit decoding algorithm. Floating-point soft-bit and LLR BP decoding show equivalent performance; the approximation incurs 0.5-dB loss, comparable to min-sum performance loss over BP. Fixed-point results show similar performance to LLR BP decoding; the approximation converges to floating-point results with one less bit of precision.     

一种低复杂度的LDPC码迭代译码算法

在LDPC码的译码算法中，和积算法性能最优但复杂性较高，最小和算法实现简单但性能与和积算法相差较多。针对这一性能与复杂度的矛盾，带有修正项的最小和算法成为研究的热点问题。文中基于一种性能与和积算法接近的修正最小和算法进行研究，对修正项的修正方式进行了简化，简化后的算法在性能上与和积算法仍非常接近，实现复杂度却比原修正最小和算法有明显的降低。     

硬件可实现的LDPC译码算法研究

低密度奇偶校验(LDPC)码有着较强的纠错能力,已被确定为第四代移动通信技术中首选码字。分析对比了几种LDPC译码算法的过程,基于硬件可实现性这一研究热点,对传统的译码算法进行了优化,提出一种易于硬件实现的LDPC译码算法。仿真结果表明:归一化最小和算法在不增加迭代次数,码长较长的情况下也有着很好的译码性能,适合在LDPC译码器的硬件实现中推广。     

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.     

基于Min-Sum近似算法的QC-LDPC译码器

由于BP算法中的非线性运算较复杂,实现中通常采用Min-Sum近似简化译码算法.针对译码过程中需要存储大量信息的问题,本文提出了一种基于Min-Sum近似算法的QC-LDPC译码器.通过重新安排Min-Sum近似算法中的运算,并将校验节点信息以一种压缩冗余的形式表示,大大减少了译码器所需的存储空间.针对QC-LDPC码校验矩阵准循环的特性,译码过程中以块为单位对信息进行更新,且可以实现多种消息传递调度策略.为进一步减少存储空间,对变量节点信息采用了非线性量化,根据密度演进理论对量化规则进行了优化.     

Graph-based iterative decoding algorithms for parity-concatenatedtrellis codes

We construct parity-concatenated trellis codes in which a trellis code is used as the inner code and a simple parity-check code is used as the outer code. From the Tanner-Wiberg-Loeliger (1981, 1996) graph representation, several iterative decoding algorithms can be derived. However, since the graph of the parity-concatenated code contains many short cycles, the conventional min-sum and sum-product algorithms cannot achieve near-optimal decoding. After some simple modifications, we obtain near-optimal iterative decoders. The modifications include either (a) introducing a normalization operation in the min-sum and sum-product algorithms or (b) cutting the short cycles which arise in the iterative Viterbi algorithm (IVA). After modification, all three algorithms can achieve near-optimal performance, but the IVA has the least average complexity. We also show that asymptotically maximum-likelihood (ML) decoding and a posteriori probability (APP) decoding can be achieved using iterative decoders with only two iterations. Unfortunately, this asymptotic behavior is only exhibited when the bit-energy-to-noise ratio is above the cutoff rate. Simulation results show that with trellis shaping, iterative decoding can perform within 1.2 dB of the Shannon limit at a bit error rate (BER) of 4×10^-5 for a block size of 20000 symbols. For a block size of 200 symbols, iterative decoding can perform within 2.1 dB of the Shannon limit     

New insights into weighted bit-flipping decoding

A natural relationship between weighted bit-flipping (WBF) decoding and belief-propagation-like (BP-like) decoding is explored. This understanding can help us develop WBF algorithms from BP-like algorithms. For min-sum decoding, one can find that its WBF algorithm is the algorithm proposed by Jiang et al. For BP decoding, we propose a new WBF algorithm and show its performance advantage. The proposed WBF algorithms are parallelized to achieve rapid convergence. Two efficient simulation-based procedures are proposed for the optimization of the associated thresholds.     

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。     

On implementation of min-sum algorithm and its modifications for decoding low-density Parity-check (LDPC) codes

The effects of clipping and quantization on the performance of the min-sum algorithm for the decoding of low-density parity-check (LDPC) codes at short and intermediate block lengths are studied. It is shown that in many cases, only four quantization bits suffice to obtain close to ideal performance over a wide range of signal-to-noise ratios. Moreover, we propose modifications to the min-sum algorithm that improve the performance by a few tenths of a decibel with just a small increase in decoding complexity. A quantized version of these modified algorithms is also studied. It is shown that, when optimized, modified quantized min-sum algorithms perform very close to, and in some cases even slightly outperform, the ideal belief-propagation algorithm at observed error rates.