Rayleigh衰落信道中分组码最大似然译码的性能

摆脱了传统代数译码方法,对分组码进行基于广义阵列码（ＧＡＣ）结构的网格译码（Ｔｒｅｌｌｉｓ ｄｅｃｏｄｉｎｇ）,研究了Ｒａｙｌｅｉｇｈ衰落信道中分组码与这种软判决最大似然译码方法相结合的性能。模拟结果显示。分组码采用网格译码,可为移动衰落信道带来显著的性能改善。     

Efficient maximum likelihood decoding of linear block codes using a trellis

It is shown that soft decision maximum likelihood decoding of any(n,k)linear block code overGF(q)can be accomplished using the Viterbi algorithm applied to a trellis with no more thanq^{(n-k)}states. For cyclic codes, the trellis is periodic. When this technique is applied to the decoding of product codes, the number of states in the trellis can be much fewer thanq^{n-k}. For a binary(n,n - 1)single parity check code, the Viterbi algorithm is equivalent to the Wagner decoding algorithm.     

Graph-based iterative decoding algorithms for parity-concatenatedtrellis codes

We construct parity-concatenated trellis codes in which a trellis code is used as the inner code and a simple parity-check code is used as the outer code. From the Tanner-Wiberg-Loeliger (1981, 1996) graph representation, several iterative decoding algorithms can be derived. However, since the graph of the parity-concatenated code contains many short cycles, the conventional min-sum and sum-product algorithms cannot achieve near-optimal decoding. After some simple modifications, we obtain near-optimal iterative decoders. The modifications include either (a) introducing a normalization operation in the min-sum and sum-product algorithms or (b) cutting the short cycles which arise in the iterative Viterbi algorithm (IVA). After modification, all three algorithms can achieve near-optimal performance, but the IVA has the least average complexity. We also show that asymptotically maximum-likelihood (ML) decoding and a posteriori probability (APP) decoding can be achieved using iterative decoders with only two iterations. Unfortunately, this asymptotic behavior is only exhibited when the bit-energy-to-noise ratio is above the cutoff rate. Simulation results show that with trellis shaping, iterative decoding can perform within 1.2 dB of the Shannon limit at a bit error rate (BER) of 4×10^-5 for a block size of 20000 symbols. For a block size of 200 symbols, iterative decoding can perform within 2.1 dB of the Shannon limit     

Design and decoding of optimal high-rate convolutional codes

This correspondence deals with the design and decoding of high-rate convolutional codes. After proving that every (n,n-1) convolutional code can be reduced to a structure that concatenates a block encoder associated to the parallel edges with a convolutional encoder defining the trellis section, the results of an exhaustive search for the optimal (n,n-1) convolutional codes is presented through various tables of best high-rate codes. The search is also extended to find the "best" recursive systematic convolutional encoders to be used as component encoders of parallel concatenated "turbo" codes. A decoding algorithm working on the dual code is introduced (in both multiplicative and additive form), by showing that changing in a proper way the representation of the soft information passed between constituent decoders in the iterative decoding process, the soft-input soft-output (SISO) modules of the decoder based on the dual code become equal to those used for the original code. A new technique to terminate the code trellis that significantly reduces the rate loss induced by the addition of terminating bits is described. Finally, an inverse puncturing technique applied to the highest rate "mother" code to yield a sequence of almost optimal codes with decreasing rates is proposed. Simulation results applied to the case of parallel concatenated codes show the significant advantages of the newly found codes in terms of performance and decoding complexity.     

软判决译码研究进展

本文综述了软判决译码研究的发展概况，全文分四部分。第一部分简单地回顾了构造Ｓｈａｎｎｏｎ码的发展概况；第二部分在前一部分基础上叙述了线性分组码软判决译码的一般研究的状况；第三部分讨论了基于网络图上的线性分组码软判决译码的发展概况；最后描述了Ｔｒｕｂｏ码与迭代反馈软判决译码的研究状况与今后的发展方向     

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.     

Relation between encoder and syndrome former variables and symbol reliability estimation using a syndrome trellis

We derive a linear correspondence between the variables of an encoder and those of a corresponding syndrome former. Using the derived correspondence, we show that the log-likelihood ratio of an information bit conditioned on a received sequence can be equally calculated using the syndrome trellis. It is shown that the proposed method also applies to recursive systematic convolutional codes which are typical constituent codes for turbo codes. Moreover, we show that soft-in syndrome decoding considering a priori probabilities of information bits is possible in the same way as for Viterbi decoding based on the code trellis. Hence, the proposed method can be applied to iterative decoding such as turbo decoding. We also show that the proposed method is effective for high-rate codes by making use of trellis modification.     

Trellis‐Based Decoding of High‐Dimensional Block Turbo Codes

This paper introduces an efficient iterative decoding method for high‐dimensional block turbo codes. To improve the decoding performance, we modified the soft decision Viterbi decoding algorithm, which is a trellis‐based method. The iteration number can be significantly reduced in the soft output decoding process by applying multiple usage of extrinsic reliability information from all available axes and appropriately normalizing them. Our simulation results reveal that the proposed decoding process needs only about 30% of the iterations required to obtain the same performance with the conventional method at a bit error rate range of 10^?5 to 10^?6.     

Application of efficient Chase algorithm in decoding of generalized low-density parity-check codes

We consider the iterative decoding of generalized low-density (GLD) parity-check codes where, rather than employ an optimal subcode decoder, a Chase (1972) algorithm decoder more commonly associated with "turbo product codes" is used. GLD codes are low-density graph codes in which the constraint nodes are other than single parity-checks. For extended Hamming-based GLD codes, we use bit error rates derived by simulation to demonstrate this new strategy to be successful at higher code rates. For long block lengths, good performance close to capacity is possible with decoding costs reduced further since the Chase decoder employed is an efficient implementation.     

Single-block coded modulation for MINO systems

This paper introduces a new class of space-time codes that achieve coding gain without a trellis or any form of inter-block dependency. The construction of the new codes starts from an existing (parent) space-time block code (STBC). Then by increasing the constellation size followed by expurgation of the expanded codebook, a better code is obtained at the original transmission rate. This method can be applied to a wide variety of space-time block codes, including orthogonal codes and quasi-orthogonal codes. A multi-stage design algorithm is presented, and for orthogonal parent codes, an efficient decoding algorithm is developed, and its decoding complexity is analyzed. Despite altering the regular structure of the orthogonal code, the decoding complexity is only affected by a constant factor.     

Closest coset decoding of |u|u+v| codes

The general concept of closest coset decoding (CCD) is presented, and a soft-decoding technique for block codes that is based on partitioning a code into a subcode and its cosets is described. The computational complexity of the CCD algorithm is significantly less than that required if a maximum-likelihood detector (MLD) is used. A set-partitioning procedure and details of the CCD algorithm for soft decoding of |u|u+v| codes are presented. Upper bounds on the bit-error-rate (BER) performance of the proposed algorithm are combined, and numerical results and computer simulation tests for the BER performance of second-order Reed-Muller codes of length 16 and 32 are presented. The algorithm is a suboptimum decoding scheme and, in the range of signal-to-noise-power-density ratios of interest, its BER performance is only a few tenths of a dB inferior to the performance of the MLD for the codes examined     

Selective trellis pruning for block codes: theory and application to iterative decoding

In this paper we present a new method to simplify trellis‐based decoding of linear block codes. Our method is based on removing (pruning) some edges and states from the trellis representation of the code; the trellis is pruned through some hard‐decisions that are made on received bits whose likelihood exceeds a predefined threshold. We show in this paper how this simplification can be accounted for by a new generator matrix (and sometimes a coset leader) that totally parametrize the new trellis, or, equivalently, the set of allowed codewords after simplification. Extensive simulations show that significant computational savings can be achieved, at a very small loss in coding performance, as long as the operating point and threshold are carefully chosen. Moreover, the application of this technique to iterative decoding of product codes is outlined, and our results show that the simplifications do not hinder the convergence, again, as long as proper parameters are chosen. Copyright © 2007 John Wiley & Sons, Ltd.     

Soft-Output BEAST Decoding With Application to Product Codes

A Bidirectional Efficient Algorithm for Searching code Trees (BEAST) is proposed for efficient soft-output decoding of block codes and concatenated block codes. BEAST operates on trees corresponding to the minimal trellis of a block code and finds a list of the most probable codewords. The complexity of the BEAST search is significantly lower than the complexity of trellis-based algorithms, such as the Viterbi algorithm and its list generalizations. The outputs of BEAST, a list of best codewords and their metrics, are used to obtain approximate a posteriori probabilities (APPs) of the transmitted symbols, yielding a soft-input soft-output (SISO) symbol decoder referred to as the BEAST-APP decoder. This decoder is employed as a component decoder in iterative schemes for decoding of product and incomplete product codes. Its performance and convergence behavior are investigated using extrinsic information transfer (EXIT) charts and compared to existing decoding schemes. It is shown that the BEAST-APP decoder achieves performances close to the Bahl–Cocke–Jelinek–Raviv (BCJR) decoder with a substantially lower computational complexity.      

Error correcting RLL codes using high rate RSC or turbo code

A construction method of error correcting run-length limited (EC-RLL) codes using a high rate recursive systematic code (RSC) or turbo code for storage systems is proposed. The EC-RLL codes can be constructed by a concatenating conventional modulation code and RSC or turbo code with puncturing. The benefit of the scheme is the utilisation of soft decision and iterative detection algorithms for decoding     

并行级联分组码的一种新的迭代译码算法

并行级联分组码比串行级联分组码具有更高的码率,基于LLR计算的Turbo迭代译码算法使其内外分量码均做到了软判决译码。通过引入校正因子a（m）,将接收信息与子译码器的输出软信息进行线性叠加反馈能在省去繁琐的LLR计算的情况下实现并行级联分组码的Turbo迭代译码。仿真研究表明,若将译码器的输出进行简单的相关运算,可进一步改善译码器性能。     

MB-OFDM UWB通信空时网格编码误码率分析

 本文研究多带正交频分复用瑞利衰落信道中,空时网格编码发射天线间空间相关性的分集性能.空时网格编码将单个输出的编码符号转换成多个编码符号,并通过多个发射天线传输,在接收端,Viterbi优化软判决算法用于译码.我们分析了MB-OFDM系统在quasi-static和interleaved两种信道中相关空间衰落对误码率的影响.在空间相关性较小时,分集阶数能得到保持;而在空间相关性较大时,interleaved信道能保持分集阶数,quasi-static信道的分集阶数将减小.空时编码总体上对空间相关性表现出鲁棒性.     

Iterative reliability-based decoding of linear block codes with adaptive belief propagation

In this letter, an iterative decoding algorithm for linear block codes combining reliability-based decoding with adaptive belief propagation decoding is proposed. At each iteration, the soft output values delivered by the adaptive belief propagation algorithm are used as reliability values to perform reduced order reliability-based decoding of the code considered. This approach allows to bridge the gap between the error performance achieved by the lower order reliability-based decoding algorithms which remain sub-optimum, and the maximum likelihood decoding, which is too complex to be implemented for most codes employed in practice. Simulations results for various linear block codes are given and elaborated.     

Parity-Check Density Versus Performance of Binary Linear Block Codes: New Bounds and Applications

The moderate complexity of low-density parity-check (LDPC) codes under iterative decoding is attributed to the sparseness of their parity-check matrices. It is therefore of interest to consider how sparse parity-check matrices of binary linear block codes can be a function of the gap between their achievable rates and the channel capacity. This issue was addressed by Sason and Urbanke, and it is revisited in this paper. The remarkable performance of LDPC codes under practical and suboptimal decoding algorithms motivates the assessment of the inherent loss in performance which is attributed to the structure of the code or ensemble under maximum-likelihood (ML) decoding, and the additional loss which is imposed by the suboptimality of the decoder. These issues are addressed by obtaining upper bounds on the achievable rates of binary linear block codes, and lower bounds on the asymptotic density of their parity-check matrices as a function of the gap between their achievable rates and the channel capacity; these bounds are valid under ML decoding, and hence, they are valid for any suboptimal decoding algorithm. The new bounds improve on previously reported results by Burshtein and by Sason and Urbanke, and they hold for the case where the transmission takes place over an arbitrary memoryless binary-input output-symmetric (MBIOS) channel. The significance of these information-theoretic bounds is in assessing the tradeoff between the asymptotic performance of LDPC codes and their decoding complexity (per iteration) under message-passing decoding. They are also helpful in studying the potential achievable rates of ensembles of LDPC codes under optimal decoding; by comparing these thresholds with those calculated by the density evolution technique, one obtains a measure for the asymptotic suboptimality of iterative decoding algorithms