TD-LTE系统咬尾卷积码译码算法研究

在TD-LTE系统中,要获得准确可靠的信道传输,就要在发送端采用差错控制编码。而卷积码作为一种前向纠错技术被应用于很多现代通信系统中,此外采用卷积码编码的数据在接收端通常都采用Viterbi译码来实现。首先介绍了咬尾卷积码编码原理,然后研究了译码的两种方法并在此基础上提出改进算法,最后通过性能仿真以及译码复杂度的比较来分析这三种译码算法。     

TD-LTE系统中咬尾卷积码译码器的FPGA实现

在LTE中,为了获得正确无误的数据传输,要采用差错控制编码技术。LTE中是采用Viterbi和Turbo加速器来实现前向纠错。咬尾卷积码保证格形起始和终止于某个相同的状态,它具有不要求传输任何额外比特的优点。本文提出一种在FPGA中实现的咬尾卷积码的Viterbi译码算法,并在Xilinx的XC3S500E芯片上实现了该算法,最后对该算法性能进行了分析。     

一种咬尾卷积译码的修正算法

在LTE系统中,物理广播信道和物理下行控制信道均采用了咬尾卷积编码,咬尾卷积编码拥有很多优良的性能.针对咬尾卷积译码,提出了一种基于Viterbi译码的修正算法——正逆序结合译码算法,根据分支度量确定误码在数据帧的分布,最后确定采用正序还是逆序译码结果.仿真结果表明,Viterbi的修正方法有效地降低了系统误码率,而且具有普适性,适合应用于LTE系统.     

基于Viterbi-双向搜索的咬尾码最大似然译码算法

传统咬尾码最大似然(ML)译码算法在译码时存在两个问题:复杂度高和消耗存储空间大。针对这两个问题,该文提出了一种基于Viterbi算法和双向搜索算法的最大似然译码算法。新算法利用Viterbi算法得到的幸存路径度量值与最大似然咬尾路径度量值的关系,删除不可能的起始状态及其对应的咬尾格形子图,缩小搜索空间;然后利用双向搜索算法中门限值与最大似然咬尾路径度量值的关系来降低双向搜索算法的复杂度,从而得到一种在咬尾格形图上高效率的最大似然译码算法。新的最大似然译码算法不仅降低了译码复杂度,同时降低了译码器对存储空间的需求。     

低复杂度的TBCC自适应循环VA译码算法

咬尾是一种将卷积码转换为块码的技术,它消除了归零状态所造成的码率损失,同时避免了截尾带来的性能降低,在短块编码中具有明显优势.针对咬尾卷积码(TBCC)现有译码算法复杂度过大和收敛性问题,提出一种低复杂度的TBCC自适应循环维特比(VA)译码算法.该算法根据信道变化自适应调整译码迭代次数,使咬尾路径收敛到最佳.通过仿真...     

LTE系统的Viterbi译码算法仿真及DSP实现

文章基于长期演进(LTE)系统的咬尾卷积码,通过对多种Viterbi译码算法的仿真比较,为时分长期演进(TD-LTE)测试系统选择了一种最优的Viterbi译码算法,并在TMS320C64x数字信号处理器(DSP)中实现了这种算法.提出了一些具体的软件优化策略和技巧,对加比选蝶形运算进行了大量简化.通过译码程序在CCS3.3中的运行结果,验证了该算法及优化策略和技巧的可行性和有效性.     

大约束度卷积码快速译码方法的研究

针对Viterbi译码算法的计算复杂度随着卷积码约束长度的增加呈指数增加,译码延迟过大,只适用于约束长度较小的卷积码译码的缺陷,提出了适用于大约束度的卷积码译码方法.采用了改进粒子群优化算法,弥补传统粒子群优化算法在解决离散问题方面的缺陷--对卷积码快速译码.该方法通过设定种群规模M来确定译码路径数,极大地缩小了译码网格中的路径搜索范围,使译码延迟减小,更适用于约束长度较大的卷积码.还提出了译码宽度自适应的卷积码译码方法,对Viterbi译码算法进行了改进,把固定的译码路径宽度改进为随信道噪声的变化而变化,大大降低译码计算复杂度.仿真实验表明提出的2种译码方法的有效性.     

LTE中卷积码的译码器设计与FPGA实现

基于长期演进（LTE）的Tail—biting卷积码,介绍了维特比译码算法,它是一种最优的卷积码译码算法。由于Tail—biting卷积码的循环特性,采用固定延迟译码的方法,降低了译码复杂度。通过使用全并行的结构及简单的回溯存储方法,设计了一个具有高速和低复杂度的固定延迟译码器。在FPGA上实现并验证,验证结果表明译码器的性能满足了LTE系统的要求。     

一种低复杂度咬尾卷积码译码算法﹡

对于咬尾卷积码的译码,传统的最大似然译码算法需要遍历每个可能的起始状态对应的咬尾格形子图,译码复杂度过高.循环维特比算法是一种有效的低复杂度次优译码算法.通过对循环维特比算法中的循环陷阱进行研究,提出了一种新的循环陷阱检测方法,利用对循环陷阱的检测可以减少冗余迭代;同时利用最大似然咬尾路径对非似然起始状态进行排除,极大的缩小了循环维特比算法中译码搜索空间.在此基础上得到了一种低复杂度的译码算法.     

基于可信位置排序的咬尾卷积码译码算法

咬尾卷积码的传统译码算法没有考虑咬尾格形图的循环性,译码起始位置固定,译码效率相对较低。该文首次证明了咬尾卷积码基于格形图的译码算法与译码起始位置无关,即从任意位置开始译码得到的最优咬尾路径即为全局最优咬尾路径。基于此提出一种基于可信位置排序的咬尾卷积码译码算法。新算法利用咬尾格形图的循环性,根据接收到的信道输出序列估算每个译码起始位置的可靠性,从而选择一个可靠性最高的译码起始位置。和传统译码算法相比,所提算法具有更快的收敛速度。     

Boosting the error performance of suboptimal tailbiting decoders

Tailbiting is an attractive method to terminate convolutional codes without reducing the code rate. Maximum-likelihood and exact a posteriori probability decoding of tailbiting codes implies, however, a large computational complexity. Therefore, suboptimal decoding methods are often used in practical coding schemes. It is shown that suboptimal decoding methods work better when the slope of the active distances of the generating convolutional encoder is large. Moreover, it is shown that considering quasi-cyclic shifts of the received channel output can increase the performance of suboptimal tailbiting decoders. The findings are most relevant to tailbiting codes where the number of states is not small relative to the block length.     

An efficiently implementable maximum likelihood decoding algorithm for tailbiting codes

Convolutional tailbiting codes are widely used in mobile systems to perform error-correcting strategies of data and control information. Unlike zero tail codes, tailbiting codes do not reset the encoder memory at the end of each data block, improving the code efficiency for short block lengths. The objective of this work is to propose a low-complexity maximum likelihood decoding algorithm for convolutional tailbiting codes based on the Viterbi algorithm. The performance of the proposed solution is compared to that of another maximum likelihood decoding strategy which is based on the A* algorithm. The computational load and the memory requirements of both algorithms are also analysed in order to perform a fair comparison between them. Numerical results considering realistic transmission conditions show the lower memory requirements of the proposed solution, which makes its implementation more suitable for devices with limited resources.     

Low-complexity ML decoding for convolutional tail-biting codes

Recently, a maximum-likelihood (ML) decoding algorithm with two phases has been proposed for convolutional tailbiting codes [1]. The first phase applies the Viterbi algorithm to obtain the trellis information, and then the second phase employs the algorithm A* to find the ML solution. In this work, we improve the complexity of the algorithm A* by using a new evaluation function. Simulations showed that the improved A* algorithm has over 5 times less average decoding complexity in the second phase when E_b/N₀? 4 dB.     

On dualizing trellis-based APP decoding algorithms

The trellis of a finite Abelian group code is locally (i.e., trellis section by trellis section) related to the trellis of the corresponding dual group code which allows one to express the basic operations of the a posteriori probability (APP) decoding algorithm (defined on a single trellis section of the primal trellis) in terms of the corresponding dual trellis section. Using this local approach, any algorithm employing the same type of operations as the APP algorithm can, thus, be dualized, even if the global dual code does not exist (e.g., nongroup codes represented by a group trellis). Given this, the complexity advantage of the dual approach for high-rate codes can be generalized to a broader class of APP decoding algorithms, including suboptimum algorithms approximating the true APP, which may be more attractive in practical applications due to their reduced complexity. Moreover, the local approach opens the way for mixed approaches where the operations of the APP algorithm are not exclusively performed on the primal or dual trellis. This is inevitable if the code does not possess a trellis consisting solely of group trellis sections as, e.g., for certain terminated group or ring codes. The complexity reduction offered by applying dualization is evaluated. As examples, we give a dual implementation of a suboptimum APP decoding algorithm for tailbiting convolutional codes, as well as dual implementations of APP algorithms of the sliding-window type. Moreover, we evaluate their performance for decoding usual tailbiting codes or convolutional codes, respectively, as well as their performance as component decoders in iteratively decoded parallel concatenated schemes.     

Two decoding algorithms for tailbiting codes

The paper presents two efficient Viterbi decoding-based suboptimal algorithms for tailbiting codes. The first algorithm, the wrap-around Viterbi algorithm (WAVA), falls into the circular decoding category. It processes the tailbiting trellis iteratively, explores the initial state of the transmitted sequence through continuous Viterbi decoding, and improves the decoding decision with iterations. A sufficient condition for the decision to be optimal is derived. For long tailbiting codes, the WAVA gives essentially optimal performance with about one round of Viterbi trial. For short- and medium-length tailbiting codes, simulations show that the WAVA achieves closer-to-optimum performance with fewer decoding stages compared with the other suboptimal circular decoding algorithms. The second algorithm, the bidirectional Viterbi algorithm (BVA), employs two wrap-around Viterbi decoders to process the tailbiting trellis from both ends in opposite directions. The surviving paths from the two decoders are combined to form composite paths once the decoders meet in the middle of the trellis. The composite paths at each stage thereafter serve as candidates for decision update. The bidirectional process improves the error performance and shortens the decoding latency of unidirectional decoding with additional storage and computation requirements. Simulation results show that both proposed algorithms effectively achieve practically optimum performance for tailbiting codes of any length.     

On error exponents for woven convolutional codes with one tailbiting component code

An error exponent for woven convolutional codes (WCC) with one tailbiting component code is derived. This error exponent is compared with that of the original WCC. It is shown that for WCC with outer warp, a better error exponent is obtained if the inner code is terminated with the tailbiting method. Furthermore, it is shown that the decoding error probability decreases exponentially with the square of the memory of the constituent convolutional encoders, while the decoding complexity grows exponentially only with the memory.     

Two step SOVA-based decoding algorithm for tailbiting codes

In this work we propose a novel decoding algorithm for tailbiting convolutional codes and evaluate its performance over different channels. The proposed method consists on a fixed two-step Viterbi decoding of the received data. In the first step, an estimation of the most likely state is performed based on a SOVA decoding. The second step consists of a conventional Viterbi decoding that employs the state estimated in the previous step as the initial and final states of the trellis. Simulations results show a performance close to that of maximum-likelihood decoding.     

A note on tailbiting codes and their feedback encoders

Tailbiting codes encoded by feedback convolutional encoders are studied. A condition for when tailbiting will work is given and it is described how the encoder starting state can be obtained for feedback encoders in both controller and observer canonical forms. Finally, results from a search for systematic feedback encoders that encode tailbiting codes with good decoding bit error probabilities are presented     

LTE系统中截尾卷积码译码算法研究及仿真性能分析

在LTE的下行控制信道和广播信道中采用了截尾卷积编码方式,本文介绍了LTE系统中采用的卷积编码器以及广播信道架构,详细分析了截尾卷积码的两种常用译码算法,文章提出了一种改进循环译码算法,在基于LTE-PBCH信道链路中对各种译码算法进行仿真,仿真测试结果表明,截尾卷积码可满足LTE信道对高速率业务的要求,双回溯循环译码算法的译码性能最佳。