BCH-Turbo编码的设计与仿真实现

本文针对Turbo码在低信噪比下迭代次数多、译码时延长问题,在分析了Turbo码的编译码原理和算法基础上,提出一种可以有效降低平均迭代次数、减少译码时延的基于BCH迭代停止准则的Turbo码迭代译码的设计方案。本方案采用BCH码作为Turbo迭代译码的停止准则。并对每一个分量译码器结果都进行判断。可提前停止迭代。通过Monte Carlo仿真表明在AWGN信道下,误码率有所降低。Turbo码译码的平均迭代次数与交叉熵准则相比有明显下降。本文还分析了BCH码编码效率和分组长度的选择对系统性能的影响。     

BCH-Turbo码的设计与仿真实现

本文针对Turbo码在低信噪比下迭代次数多、译码时延长问题,在分析了Turbo码的编译码原理和算法基础上,提出一种可以有效降低平均迭代次数、减少译码时延的基于BCH迭代停止准则的Turbo码迭代译码的设计方案.本方案采用BCH码作为Turbo迭代译码的停止准则,并对每一个分量译码器结果都进行判断,可提前停止迭代.通过Monte Carlo仿真表明在AWGN信道下,误码率有所降低,Turbo码译码的平均迭代次数与交叉熵准则相比有明显下降.本文还分析了BCH码编码效率和分组长度的选择对系统性能的影响.     

一种用于LTE的提前终止Turbo码算法仿真

Turbo编码是Long Time Evolution(LTE)定义的信道编码形式,可以获得逼近香香农理论极限的译码性能,目前广泛应用于第3代和第4代移动通信系统中。为了解决Turbo译码时延长和计算复杂度的问题,研究了一种可以应用于LTE系统中的基于循环冗余校验的改进Turbo译码算法,在Turbo译码器中增加循环冗余校验(Cyclic Redundancy Check,CRC)提前终止准则,在译码迭代结束时检验是否存在错误比特码,在无CRC校验错误时提前终止迭代译码,在不影响译码性能的同时降低译码复杂度。Matlab仿真结果表明,与固定迭代次数的Turbo码算法相比,译码延迟得到了显著改善。     

一种适用于Turbo译码的新型迭代停止算法

针对Turbo码迭代译码延时大的问题,本文提出了一种加速译码的新型迭代停止方法,该方法利用了两个分量译码器输出的对数似然比LLR(Logarithm Likelihood Ratio)的统计特性,简称为LCB(LLR Characters Based)算法.同现存的经典停止准则相比,在相同的比特误码率性能下,新方法有效的降低了译码平均迭代次数,加速了Turbo码译码,可应用于流媒体信号传输系统.     

一种简单的Turbo码的迭代停止判据

Turbo码提出之后,由于其优异的性能成为研究热点。但为了得到其优异的性能,在译码过程中需要进行多次迭代,这造成巨大的译码延时。在不影响性能的情况下,为减少译码延时,迭代停止在Turbo码迭代译码过程中十分重要。提出一种基于似然比绝对值大于某一门限的比特数目的迭代停止判据。并且给出平均迭代次数、译码性能的仿真。仿真结果证明该算法的有效性。     

Turbo译码算法的改进及其在卫星通信链路中的仿真

Turbo码的优越性使其在3G、4G及卫星通信等领域应用广泛。文章在分析了几种算法的基础上对Max-log-MAP算法进行了改进，引入迭代译码停止准则，并对算法进行了定点和加滑动窗处理，从而降低了译码复杂度，减少了译码时间，使Turbo译码容易通过硬件实现。     

软输入/软输出迭代译码算法的研究与设计

对软输入/软输出迭代译码算法进行了理论研究,分析该算法的共性,并以Turbo 乘积码的性能仿真说明迭代译码对译码性能的影响,还以理论研究为基础,对迭代译码算法进行了硬件设计,重点探讨了Turbo 乘积码的译码算法硬件设计.     

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乘积码仿真研究

Turbo codes译码算法的核心是软输入/软输出迭代译码,把这种思想应用于乘积码的译码中,得到了Turbo乘积码(TPC)迭代译码算法。介绍了基于扩展汉明码的乘积码的迭代译码算法,时该算法在AWGN信道中的译码性能进行了仿真。     

Turbo码译码性能的研究

Turbo码基于随机编码的思想,将卷积编码与交织器结合使其性能接近香农极限,所以Turbo码被3G移动通信系统所采纳并在深海通信、卫星通信等功率受限领域得以应用.文中介绍了Turbo码的译码原理并分析了几种主要的译码方法,在Matlab环境下对Turbo码进行了仿真研究,分析了不同的迭代次数、译码算法、帧长、码率及分量码对其译码性能的影响.仿真结果表明Turbo码纠错性能优良,具有广泛的应用前景.     

一种应用于LTE系统的Viterbi译码算法

LTE(long term evolution,长期演进)系统中采用了咬尾卷积码和Turbo码来实现前向纠错,Viterbi译码是卷积码的一种杰出的译码算法,它是一种最大似然译码方法。本文基于LTE系统中的咬尾卷积码,详细分析了几种较成熟的Viterbi译码算法,并综合现有算法,提出了一种改进算法,减小了译码计算的复杂度。仿真结果表明,改进算法在降低译码计算复杂度的同时还降低了译码误比特率,因此非常适合LTE系统的译码要求。     

Design of a transport triggered vector processor for turbo decoding

In order to meet the requirement of high data rates for next generation wireless systems, efficient implementations of receiver algorithms are essential. On the other hand, faster time-to-market motivates the investigation of programmable implementations. This paper presents a novel design of a programmable turbo decoder as an application-specific instruction-set processor (ASIP) using transport triggered architecture (TTA). The processor architecture is designed in such a manner that it can be programmed with high level language to support different suboptimal maximum a posteriori (MAP) algorithms in a single TTA processor. The design enables the designer to change the algorithms according to the frame error rate performance requirement. A quadratic polynomial permutation interleaver is used for contention-free memory access and to make the processor 3GPP LTE compliant. Several optimization techniques to enable real time processing on programmable platforms are introduced. The essential parts of the turbo decoding algorithm are designed with vector function units. Unlike most other turbo decoder ASIPs, high level language is used to program the processor to meet the time-to-market requirements. With a single iteration, 68.35 Mbps decoding speed is achieved for the max-log-MAP algorithm at a clock frequency of 210 MHz on 90 nm technology.     

基于无冲突多项式交织器的高速并行Turbo解码器

提出了基于高次多项式无冲突交织器的Turbo码并行解码的优化实现方法,解码器采用MAX-Log-MAP算法,完成了从Matlab算法设计验证到RTL设计、FPGA验证,并在LTE无线通信链路中验证.设计的Turbo并行高速解码器半次迭代的效率为6.9 bit/cycle,在最高迭代为5.5次、时钟频率为309MHz下,达到207Mb/s的吞吐率,满足高速无线通信系统的要求,交织和解交织采用存储器映射方法.该设计节约了计算电路和存储量.     

A novel BTC decoding algorithm based on the genetic algorithm in optical communication systems

Combining the advantages of both the genetic algorithm （GA） and the chase decoding algorithm, a novel improved decoding algorithm of the block turbo code （BTC） with lower computation complexity and more rapid decoding speed is proposed in order to meet the developing demands of optical communication systems. Compared with the traditional chase decoding algorithm, the computation complexity can be reduced and the decoding speed can be accelerated by applying the novel algorithm. The simulation results show that the net coding gain （NCG） of the novel BTC decoding algorithm is 1.1 dB more than that of the traditional chase decoding algorithm at the bit error rate （BER） of 10^-6. Therefore, the novel decoding algorithm has better decoding correction-error performance and is suitable for the BTC in optical communication systems.     

Turbo Decoding on the Binary Erasure Channel: Finite-Length Analysis and Turbo Stopping Sets

This paper is devoted to the finite-length analysis of turbo decoding over the binary erasure channel (BEC). The performance of iterative belief-propagation decoding of low-density parity-check (LDPC) codes over the BEC can be characterized in terms of stopping sets. We describe turbo decoding on the BEC which is simpler than turbo decoding on other channels. We then adapt the concept of stopping sets to turbo decoding and state an exact condition for decoding failure. Apply turbo decoding until the transmitted codeword has been recovered, or the decoder fails to progress further. Then the set of erased positions that will remain when the decoder stops is equal to the unique maximum-size turbo stopping set which is also a subset of the set of erased positions. Furthermore, we present some improvements of the basic turbo decoding algorithm on the BEC. The proposed improved turbo decoding algorithm has substantially better error performance as illustrated by the given simulation results. Finally, we give an expression for the turbo stopping set size enumerating function under the uniform interleaver assumption, and an efficient enumeration algorithm of small-size turbo stopping sets for a particular interleaver. The solution is based on the algorithm proposed by Garello et al. in 2001 to compute an exhaustive list of all low-weight codewords in a turbo code.     

Turbo decoding as an instance of Pearl's “beliefpropagation” algorithm

We describe the close connection between the now celebrated iterative turbo decoding algorithm of Berrou et al. (1993) and an algorithm that has been well known in the artificial intelligence community for a decade, but which is relatively unknown to information theorists: Pearl's (1982) belief propagation algorithm. We see that if Pearl's algorithm is applied to the “belief network” of a parallel concatenation of two or more codes, the turbo decoding algorithm immediately results. Unfortunately, however, this belief diagram has loops, and Pearl only proved that his algorithm works when there are no loops, so an explanation of the experimental performance of turbo decoding is still lacking. However, we also show that Pearl's algorithm can be used to routinely derive previously known iterative, but suboptimal, decoding algorithms for a number of other error-control systems, including Gallager's (1962) low-density parity-check codes, serially concatenated codes, and product codes. Thus, belief propagation provides a very attractive general methodology for devising low-complexity iterative decoding algorithms for hybrid coded systems     

Turbo decoding of product codes using adaptive belief propagation

The adaptive belief propagation (ABP) algorithm was recently proposed by Jiang and Narayanan for the soft decoding of Reed-Solomon (RS) codes. In this paper, simplified versions of this algorithm are investigated for the turbo decoding of product codes. The complexity of the turbo-oriented adaptive belief propagation (TAB) algorithm is significantly reduced by moving the matrix adaptation step outside of the belief propagation iteration loop. A reduced-complexity version of the TAB algorithm that offers a trade-off between performance and complexity is also proposed. Simulation results for the turbo decoding of product codes show that belief propagation based on adaptive parity check matrices is a practical alternative to the currently very popular Chase-Pyndiah algorithm.     

Comparison of Convolutional and Turbo Coding for Broadband FWA Systems

It has been demonstrated that turbo codes substantially outperform other codes, e.g., convolutional codes, both in the non-fading additive white Gaussian noise (AWGN) channel as well as multiple-transmit and multiple-receive antenna fading channels. Moreover, it has also been reported that turbo codes perform very well in fast fading channels, but perform somewhat poorly on slow and block fading channels of which the broadband fixed wireless access (FWA) channel is an example. In this paper, we thoroughly compare the performance of turbo-coded and convolutional-coded broadband FWA systems both with and without antenna diversity under the condition of identical complexity for a variety of decoding algorithms. In particular, we derive mathematical expressions to characterize the complexity of turbo decoding based on state-of-the-art Log-MAP and Max-Log-MAP algorithms as well as convolutional decoding based on the Viterbi algorithm in terms of the number of equivalent addition operations. Simulation results show that turbo codes do not offer any performance advantage over convolutional codes in FWA systems without antenna diversity or FWA systems with limited antenna diversity. Indeed, turbo codes only outperform convolutional codes in FWA systems having significant antenna diversity.     

Turbo码概率-代数联合解码算法

Turbo码采用修正的BAHL et al.算法实现解码.这是一种基于软值的概率迭代解码算法.本文在保持Turbo码迭代软解码算法优点的基础上,充分利用Turbo码编码器结构这一确知条件,结合代数解码原理,提出了一种Turbo码概率-代数联合解码算法.该算法结合了概率解码和代数解码的优点,又有效避免了误差传播的发生,使Turbo码的纠错性能在原经典算法的基础上得到进一步的提高.该算法不仅为降低Turbo码的比特误码率和误差地板值提供了一种新的研究途径,而且因其更好的纠错性能而具有十分明显的实用价值.仿真实验结果显示,在比特误码率(BER)为10^-3~10^-4时,与经典Turbo码解码算法相比,采用该算法能获得0.1dB左右的编码增益.