Turbo码SOVA译码的一种新的改进算法

SOVA算法由于低复杂度和低译码延时,已成为Turbo码的实用译码算法。该文针对SOVA译码算法的软判决值不精确对译码性能的影响,借鉴非均匀量化思想,提出了一种新的改进算法。仿真结果表明,在几乎不增加译码复杂度的情况下能够明显地改善译码性能。     

SOVA算法对Viterbi算法的修正

在Viterbi算法中引入软值进行修正之后的算法称作SOVA算法(Soft Output Viterbi Algorithm)。SOVA算法在Viterbi算法的基础上,路径量度引入了比特先验信息,对每位译码比特以后验概率似然比的形式提供软输出,因而可提供更高的译码性能。特别,SOVA算法可用于级联码的迭代译码,采用Tuobo原理使不同分量码之间交换软信息,从而可显著提高这类码的纠错能力。     

Turbo码软判决输出维特比译码方案的改进

Turbo码是一种新的纠错编码,具有十分强的纠错能力。Turbo码编码端采用两个或两个以上的卷积并行级联构成,译码端则采用以基于软判决信息输入/输出的反馈迭代译码结构。译码算法是Turbo码设计的核心,现已有的两种主要的译码算法——MAP和SOVA。SOVA是一种改进的维特比算法,使其可以逐比特输出与MAP算法类似的软判决信息。该文综述了Turbo码SOVA译码的几种改进方式,并分析了这几种改进方式及仿真结果。     

Turbo码软判决输出维特比译码方案的改造

Turbo码是一种新的纠错编码，具有十分强的纠错能力。Turbo码编码端采用两个或两个以上的卷积并行级联构成，译码端则采用以基于软判决信息输入／输出的反馈迭代译码结构。译码算法是Turbo码设计的核心，现已有的两种主要的译码算法——MAP和SOVA。SOVA是一种改进的维特比算法，使其可以逐比特输出与MAP算法类似的软判决信息。该文综述了Turbo码SOVA译码的几种改进方式，并分析了这几种改进方式及仿真结果。     

Turbo码译码的改进SOVA算法

Turbo编码自1993年提出以来，由于其出色的译码性能，在编码界得了广泛关注，逐渐被吸纳到一些标准化体系中，对于Turbo码的译码问题，目前已有许多种译码算法，在传统SOVA（软输出维特比算法）译码算法的基础上，给出了一种SOVA译码的改进算法，仿真结果表明该算法在译码性能等方面具有较强的优越性。     

Turbo码软判决输出维持比译码方案的改进

Turbo码是一种新的纠错编码，具有十分强的纠错能力，Turbo码编码端采用两个或两个以上的卷积并行级联构成，译码端则采用以基于软判决信息输入/输出的反馈迭代译码结构。译码算法是Turbo码设计的核心，现巳有的两种主要的译码算法-MAP和SOVA。SOVA是一种改进的维持比算法，使其可以逐比特输出与MAP算法类似的软判决信息。该文综述了Turbo码SOVA译码的几种改进方式，并分析了这几种改进方式及仿真结果。     

基于滑窗结构的软输出维特比算法的实现

文章在对软输出维特比算法(SOVA)进行推导的基础上，分析了软信息的提取过程。同时从硬件实现的角度考虑，提出了一种基于滑动窗1：3结构的SOVA算法实现方案，该算法大大降低了算法实现复杂度和译码延迟，同时通过调整滑窗参数，可以取得与非滑窗SOVA算法几乎相同的性能。     

一种新的SOVA改进算法的定点DSP实现

该文通过引入软判决值修正函数,提出了一种新的SOVA译码算法,仿真结果表明能够明显改善译码性能。对改进算法在定点DSP上的实现进行了深入研究,分析了量化组数、有限字长效应对性能的影响及解码速度和存储容量需求等,并给出了相应的测试结果。     

基于修正路径量度的Turbo码SOVA译码算法研究

针对传统SOVA(soft output Viterbi algorithm)算法在选择错误路径概率的计算上存在的不足,提出了改进的Turbo码SOVA译码方法.根据各状态幸存路径累计量度的差值,对译码回溯深度范围内最末位的数个比特的可靠度值进行修正,然后将修正值作为软判决输出.理论推导和计算机仿真结果均表明,所提出的修正算法能提高译码性能.     

一种新的基-4SOVA译码算法

SOVA (Soft Output Viterbi Algorithm)类算法因其译码时延远低于MAP类算法已成为Turbo码的实用译码算法,为了进一步减小译码延迟,提高译码速度,该文在简单分析基-4Max-Log-MAP算法的基础上,提出了一种新的基-4SOVA算法,并进行了完整的数学推导.该算法的关键是提出了一种新...     

New Soft Output Viterbi Algorithm for Mobile Communication System

Soft output Viterbi algorithm(SOVA) is a turbo decoding algorithm that is suitable for hardware implementation. But its performance is not so good as maximum a posterior probability(MAP) algorithm. So it is very important to improve its performance. The non-correlation between minimum and maximum likelihood paths in SOVA is analyzed. The metric difference of both likelihood paths is used as iterative soft information, which is not the same as the traditional SOVA. The performance of the proposed SOVA is demonstrated by the simulations. For 1024-bit frame size and 9 iterations with signal to noise ratio from 1dB to 4dB, the experimental results show that the new SOVA algorithm obtains about more 0.4dB and 0.2dB coding gains more than the traditional SOVA and Bi-SOVA algorithms at bit error rate(BER) of 1×10~ -4 , while the latency is only half of the Bi-direction SOVA decoding.     

Complexity Reduction in SISO Decoding of Block Codes

SISO decoding for block codes can be carried out based on a trellis representation of the code. However, the complexity entailed by such decoding is most often prohibitive and thus prevents practical implementation. This paper examines a new decoding scheme based on the soft-output Viterbi algorithm (SOVA) applied to a sectionalized trellis for linear block codes. The computational complexities of the new SOVA decoder and of the conventional SOVA decoder, based on a bit-level trellis, are theoretically analyzed and derived for different linear block codes. These results are used to obtain optimum sectionalizations of a trellis for SOVA. For comparisons, the optimum sectionalizations for Maximum A Posteriori (MAP) and Maximum Logarithm MAP (Max-Log-MAP) algorithms, and their corresponding computational complexities are included. The results confirm that the new SOVA decoder is the most computationally efficient SISO decoder, in comparisons to MAP and Max-Log-MAP algorithms. The simulation results of the bit error rate (BER) performance, assuming binary phase -- shift keying (BPSK) and additive white Gaussian noise (AWGN) channel, demonstrate that the performance of the new decoding scheme is not degraded. The BER performance of iterative SOVA decoding of serially concatenated block codes shows no difference in the quality of the soft outputs of the new decoding scheme and of the conventional SOVA.     

一种基于SOVA的改进的Turbo码译码方案

本文在研究Ｔｕｒｂｏ 码反向ＳＯＶＡ（Ｓｏｆｔ－Ｏｕｔｐｕｔ ＶｉｔｅｒｂｉＡｌｇｏｒｉｔｈｍ ）译码性能的基础上,提出了一种同时利用正向和反向ＳＯＶＡ译码软输出信息的基于ＳＯＶＡ 的改进译码结构及其相应的软输出修正公式。计算机模拟结果表明,所提出的改进方案与传统的ＳＯＶＡ算法相比,其译码性能有明显的改善,并略优于Ｍａｘ－Ｌｏｇ－ＭＡＰ的性能     

等价于MAP的SOVA译码方法

不同于MAP(Maximum A Posteriori)算法,SOVA(Soft-Output Viterbi Algorithm)算法的软输出不是真正意义上的后验概率,很少有文献给出SOVA算法的完整数学解释。该文给出了一种完整的SOVA的数学表达形式,并从SOVA的数学表达出发推导出了两种等价于MAP,具有SOVA形式的译码方法,一种是Li等人(1995)给出的适用于连续传输的最佳软输出算法(Optimal Soft output Algorithm, OSA);后一种是对OSA算法的改进,后者可以得到与前者等价的软输出,但是降低了运算复杂度。     

基于MSK和LT码的联合迭代译码算法

传统的简单级联编码调制系统在译码时会造成软信息损失.提出了一种基于MSK和LT码的联合软迭代译码算法,设计了算法的系统模型.利用LT码的软译码和MSK调制的SISO算法,进行联合软迭代译码,提高了编码调制系统的性能.仿真结果表明:在误码率为10-4时,提出的算法比传统的简单级联编码调制算法约有1.5 dB的编码增益.     

一种性能优越的Turbo码SOVA译码算法

MAP译码算法性能上是最优的,但是其复杂度也是十分高的,影响了硬件的实现,介绍了一种性能上接近于MAP译码算法,复杂度上有明显减少的译码算法,并且对其进行了完善,仿真结果表明对于二进制Turbo码,改进后的译码算法与MAP算法的译码性能更为接近。     

List and soft symbol output Viterbi algorithms: extensions andcomparisons

The Viterbi algorithm (VA) is the maximum likelihood decoding algorithm for convolutionally encoded data. Improvements in the performance of a concatenated coding system that uses VA decoding (inner decoder) can be obtained when, in addition to the standard VA output, an indicator of the reliability of the VA decision is delivered to the outer stage of processing. Two different approaches of extending the VA are considered. In the first approach, the VA is extended with a soft output (SOVA) unit that calculates reliability values for each of the decoded output information symbols. In the second approach, coding gains are obtained by delivering a list of the L best estimates of the transmitted data sequence, namely the list Viterbi decoding algorithm (LVA). Our main interest is to evaluate the LVA and the SOVA in comparison with each other, determine suitable applications for both algorithms and to construct extended versions of the LVA and the SOVA with low complexity that perform the task of the other algorithm. We define a list output VA using the output symbol reliability information of the SOVA to generate a list of size L and that also has a lower complexity than the regular LVA for a long list size. We evaluate the list-SOVA in comparison to the LVA. Further, we introduce a low complexity soft symbol output viterbi algorithm that accepts the (short) list output of the LVA and calculates for each of the decoded information bits a reliability value. The complexity and the performance of the soft-LVA (LVA and soft decoding unit) is a function of the list size L. The performance of the soft-LVA and the SOVA are compared in a concatenated coding system. A new software implementation of the iterative serial version of the LVA is also included     

Non‐binary SOVA algorithms for decoding of block codes on a sectionalized trellis

In this paper, we explore computationally efficient implementations of the soft output viterbi algorithm (SOVA) applied to Soft‐Input Soft‐Output (SISO) decoding of linear block codes. In order to simplify the trellis‐based decoding of binary block codes with SOVA, we use the technique of sectionalization of the trellis, which has been successfully applied to the simplification of the MAP and Max‐Log‐MAP algorithms. Due to the branch complexity of the sectionalized trellis, we define a generalization of a non‐binary version of SOVA. However, the computational complexity of directly applying this approach remains too high for efficient implementation; we thus introduce the concept of non‐binary SOVA (NSOVA) with propagation of bit‐level reliabilities (BLR). This new algorithm is analyzed from a computational complexity viewpoint. Both serial and parallel implementations are explored. Finally, optimal sectionalizations are derived for selected codes; since the normal SOVA decoding is a particular case of NSOVA with BLR, we show that our approach is more efficient than a bit‐level trellis by showing that, for all the codes tested, the optimal trellis is a sectionalized one. Copyright © 2007 John Wiley & Sons, Ltd.