Turbo码的一种全新的SOVA译码算法

SOVA算法因其译码时延低于MAP算法已成为Turbo码的实用译码算法。本文提出了一种放弃软判决值更新处理的全新的SOVA算法。该算法的独到之处在于,综合利用对栅格图的正向和反向搜索,从而实现了通过全局路径比较来产生软输出值。仿真结果表明,与传统SOVA算法相比这种全新的SOVA算法在不会明显增加译码计算量的前提下,显著地改善了译码性能。同时,其误码率性能在高信噪比时略优于Max-Log-MAP算法,并且已经逼近MAP算法。     

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...     

低莱斯因子衰落信道下的Turbo码性能研究

基于Turbo码编译码技术,在Rician衰落信道模型下,详细研究了Turbo码Log-MAP译码算法和SOVA译码算法在低Rician因子衰落信道下的性能,并结合调制技术对整个编译码系统进行Matlab仿真。仿真结果表明,在低Rician因子信道下,Log-MAP译码算法的性能优于SOVA译码算法。     

Turbo码的一种高效改进型MAP译码算法

该文给出了一种改进型最大后验概率(MAP)译码算法用于实现并行级联卷积码(Turbo码)的最优译码。与基于对数域的Log-MAP算法相比较,该文给出的算法不引入对数域,但能够完全消除标准MAP算法在迭代过程中必须进行的大量指数和对数运算。计算机仿真结果表明,这种具有最优纠错性能的改进型MAP算法能够显著减少运行时间,其译码效率甚至优于牺牲了较多纠错性能的最快速的对数域MAP译码算法(Max-Log-MAP)。     

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。     

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码的迭代译码及在DS—CDMA和OFDM／CDMA中的应用

本文简单介绍了Turbo码基本原理、子码编码器和交织器设计等,详细地分析了Turbo码的基于MAP译码算法和基于SOVA译码算法的迭代译码方法及其性能,并与卷积码的性能进行了比较.重点论述了Turbo码在DS-CDMA移动通信系统的码率与扩频增益折衷设计、迭代译码和性能仿真结果.最后,简单论述了Turbo码在OFDM/CDMA中的应用.     

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。     

Turbo码的定点Max-Log-MAP译码算法

阐述了Turbo码的对数域迭代译码算法,并针对定点运算的特点进行了改进。仿真结果表明,采用6比特量化输入、4轮迭代的定点运算译码,其性能接近于浮点Max-Log-MAP译码算法。     

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.     

Bi-directional SOVA decoding for turbo-codes

In this letter, we present a bi directional SOVA-based decoding scheme for turbo-codes, which combines the soft-outputs provided by both forward and backward SOVA decodings. Importantly, we explain why this bi-directional decoding improves SOVA decoding. The simulation results show that bi-directional SOVA can achieve about the same performance as the Max-Log-MAP algorithm in turbo decoding     

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。     

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     

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.     

Performance of parallel and serial concatenated codes on fading channels

The performance of parallel and serial concatenated codes on frequency-nonselective fading channels is considered. The analytical average upper bounds of the code performance over Rician channels with independent fading are derived. Furthermore, the log-likelihood ratios and extrinsic information for maximum a posteriori (MAP) probability and soft-output Viterbi algorithm (SOVA) decoding methods on fading channels are developed. The derived upper bounds are evaluated and compared to the simulated bit-error rates over independent fading channels. The performance of parallel and serial codes with MAP and SOVA iterative decoding methods, with and without channel state information, is evaluated by simulation over independent and correlated fading channels. It is shown that, on correlated fading channels, the serial concatenated codes perform better than parallel concatenated codes. Furthermore, it has been demonstrated that the SOVA decoder has almost the same performance as the MAP decoder if ideal channel state information is used on correlated Rayleigh fading channels.