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.     

光传输系统中一种分组Turbo码的新颖译码算法分析

基于遗传算法与Chase译码算法的各自优势,提出了一种降低运算复杂度并加快译码速度的新颖分组Turbo码(BTC)译码算法.与传统的Chase译码算法相比,该译码算法降低了译码复杂度且加快了译码速度.仿真分析表明,该算法较传统的Chase译码算法在误码率为10-6时提高了约1.15 dB的净编码增益(NCG),具有良好的纠错性能.因而它是一种适用于光传输系统且实用性较强的新颖BTC译码算法.     

基于编码方法的Turbo乘积码译码

利用编码方法搜求Turbo乘积码译码中的候选码字,对由单纠错能力分组码作为子码的乘积码进行仿真,考察不等数量的候选码字对译码性能的影响,并对两种估计无竞争码字比特外部信息值的方式在利用编码方法及Chase—II算法搜求候选码字时所形成的性能差异进行分析。     

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.     

光通信中基于分组Turbo码的一种改进Chase译码算法

为了适应光通信发展的要求,依据分组Turbo码(BTC)传统Chase译码算法的分析,提出了一种基于不对等可靠位数的改进新译码算法。使用该算法在每次迭代时将产生一个可靠度参数对外部信息进行修正,从而提高BTC的译码性能。仿真结果表明:在误码率(BER)为10-5且迭代4次的情况下,新BTC译码算法与传统Chase译码算法相比,其净编码增益(NCG)提高了0.9dB,并且在最差情况下给系统增加的译码复杂度都不大。     

短波通信中的低复杂度时域均衡

提出了一种基于Chase算法的低复杂度时域均衡技术.首先通过传统的自适应均衡器获取软值,再利用Chase算法计算可信度找到不可靠位,同时构造测试序列,然后进行滑动窗搜索和逐比特译码.此算法在减小计算量的同时消除了译码时延,并且误码率逼近最大似然序列估计,实现了复杂度和性能的折衷.     

一种改进的Turbo乘积码译码算法

针对Turbo乘积码(TPC)译码复杂度高、运算量大的缺点,分析了一种改进的TPC译码算法。该算法以Chase迭代算法为基础,通过对错误图样重新排序产生新的测试序列,其伴随式可从前次伴随式的基础上修正一位得到,大大简化了计算步骤。在AWGN信道下对新算法进行了Matlab仿真,结果表明,改进的算法在保持译码性能基本不变的前提下,提高了译码速度,降低了译码复杂度。     

一种低复杂度的部分干扰抵消群译码算法

本文研究了在MIMO系统的接收端,采用部分干扰抵消群译码算法对接收端信号进行译码的算法,在保证码字获得满分集的同时,降低接收端的译码复杂度.通过将一个空时分组码中所包含的符号进行群组划分,对于不同的符号群组,在对接收信号经过线性滤波处理之后分别独立地进行译码操作,从而达到降低译码复杂度的目的.仿真结果显示,该算法相对于传统的最大似然译码算法,译码复杂度明显降低.     

一种(41, 21, 9)平方剩余码的快速代数译码算法

为了降低译码时的计算复杂度以及减少译码时间,该文通过对牛顿恒等式进行推导得到了(41, 21, 9) QR码不需要计算未知校验子就可求得错误位置多项式系数的代数译码算法,同时也针对改善部分客观地给出了计算复杂度的理论分析。此外,为了进一步降低译码时间,提出判定接收码字中出现不同错误个数的更简化的判断条件。仿真结果表明该文提出算法在不降低Lin算法所达到的译码性能的前提下,降低了译码时间。     

HSDPA技术中HARQ的一种新组合译码算法

文中介绍了HSDPA中组合译码的组合方案,并在传统Chase组合译码算法的基础上提出了一种改进的新组合译码算法。最后通过计算机仿真,结果表明采用新算法的HARQ在有效减少重传次数方面明显优于采用无组合译码算法的HARQ。     

一种快速软判决译码的研究

本文给出一种分组码快速软判决译码—可变门限Chase算法(VTC).采用人工智能搜索技术—A^*算法,快速生成试探序列集合,并利用已经试探译码的信息,对试探序列集合进行分类,生成试探序列的等价类及其代表,并用最优门限对候选码字进行最佳测试,可实现快速软判决译码.模拟计算表明,与已有的软判决译码算法相比,该算法的译码速度更快而译码性能完全相同.     

Geometrical and performance analysis of GMD and Chase decodingalgorithms

The overall number of nearest neighbors in bounded distance decoding (BDD) algorithms is given by N_0,eff=N₀+N _BDD. Where NBDD denotes the number of additional, non-codeword, neighbors that are generated during the (suboptimal) decoding process. We identify and enumerate the nearest neighbors associated with the original generalized minimum distance (GMD) and Chase (1972) decoding algorithms. After careful examination of the decision regions of these algorithms, we derive an approximated probability ratio between the error contribution of a noncodeword neighbor (one of N_BDD points) and a codeword nearest neighbor. For Chase algorithm 1 it is shown that the contribution to the error probability of a noncodeword nearest neighbor is a factor of 2^d-1 less than the contribution of a codeword, while for Chase algorithm 2 the factor is 2^[d/2]-1, d being the minimum Hamming distance of the code. For Chase algorithm 3 and GMD, a recursive procedure for calculating this ratio, which turns out to be nonexponential in d, is presented. This procedure can also be used for specifically identifying the error patterns associated with Chase algorithm 3 and GMD. Utilizing the probability ratio, we propose an improved approximated upper bound on the probability of error based on the union bound approach. Simulation results are given to demonstrate and support the analytical derivations     

A CASCADABLE PRAGMATIC BLOCK DECODING ALGORITHM EXPLOITING CHANNEL MEASUREMENT INFORMATION

The complexity of algorithms to perform soft decision decoding on block codes has impeded their inclusion in practical systems. A well-known class of algorithms for decoding block codes utilizing channel measurement information along with the algebraic properties of the code are the Chase algorithms.¹ In this paper a decoding method similar to Chase's third algorithm is presented. However, in this method, a single test pattern or alternate codeword makes up one stage of the decoder. The method uses information from the previous decoding(s) to assist in generating a test pattern. This single stage ‘Second Chance Algorithm’ can then be extended to a ‘Third Chance Algorithm’ (and beyond) to enhance performance. The method does not invoke the hard decision decoder as often as the Chase algorithms.     

Distance-based-decoding of block turbo codes

List-based algorithms for. decoding block turbo Codes (BTC) have gained popularity due to their low computational complexity. The normal way to calculate the soft outputs involves searching for a decision code word D and a competing codeword B. In addition, a scaling factor /spl alpha/ and an estimated reliability value /spl beta/ are used. In this letter, we present a new approach that does not require /spl alpha/ and /spl beta/. Soft outputs are generated based on the Euclidean distance property of decision code words. By using the new algorithm, we achieve better error performance with even less complexity-for certain BTCs.     

On algebraic soft-decision decoding algorithms for BCH codes

Three algebraic soft-decision decoding algorithms are presented for binary Bose-Chaudhuri-Hocquengham (BCH) codes. Two of these algorithms are based on the bounded distance (BD)+1 generalized minimum-distance (GMD) decoding presented by Berlekamp (1984), and the other is based on Chase (1972) decoding. A simple algebraic algorithm is first introduced, and it forms a common basis for the decoding algorithms presented. Next, efficient BD+1 GMD and BD+2 GMD decoding algorithms are presented. It is shown that, for binary BCH codes with odd designed-minimum-distance d and length n, both the BD+1 GMD and the BD+2 GMD decoding algorithms can be performed with complexity O(nd). The error performance of these decoding algorithms is shown to be significantly superior to that of conventional GMD decoding by computer simulation. Finally, an efficient algorithm is presented for Chase decoding of binary BCH codes. Like a one-pass GMD decoding algorithm, this algorithm produces all necessary error-locator polynomials for Chase decoding in one run     

Adaptive Chase algorithm for block turbo codes

A new adaptive implementation of the Chase algorithm for binary block codes is proposed. Compared to the two efficiently used algorithms for iterative decoding, the standard Chase and Kaneko algorithms, the proposed algorithm can give the same performance with significant complexity reduction.     

自适应量化测试序列数的分组Turbo码译码算法

针对分组Turbo码自适应Chase译码算法存在的缺陷,该文提出自适应量化测试序列数的分组Turbo码译码算法。该方法以测试序列数C为研究对象,依出错概率大小选择错误图样,并利用量化测试函数根据SNR的变化对测试序列数进行量化,从而达到直接控制译码复杂度的目的。仿真结果表明,所提出的译码算法保证了译码性能,并直接降低了译码复杂度。     

Generalization of chase algorithms for soft decision decoding of binary linear codes

Soft decision decoding of binary linear block codes transmitted over the additive white Gaussian channel (AWGN) using antipodal signaling is considered. A set of decoding algorithms called generalized Chase algorithms is proposed. In contrast to Chase algorithms, which require alfloor (d- 1)/2 rfloorbinary error-correcting decoder for decoding a binary linear block code of minimum distanced, the generalized Chase algorithms can use a binary decoder that can correct less thanlfloor ( d - 1)/2 rfloorhard errors. The Chase algorithms are particular cases of the generalized Chase algorithms. The performance of all proposed algorithms is asymptotically optimum for high signal-to-noise ratio (SNR). Simulation results for the(47, 23)quadratic residue code indicate that even for low SNR the performance level of a maximum likelihood decoder can be approached by a relatively simple decoding procedure.