Error-erasure decoding of binary cyclic codes, up to a particularinstance of the Hartmann-Tzeng bound

It is shown that error-erasure decoding for a cyclic code allows the correction of a combination of t errors and r erasures when 2t+r<σ₀; the parameter σ₀ denotes a particular instance of the Hartmann-Tzeng bound. This procedure is an improvement on the error-erasure decoding algorithm developed by G.D. Forney (1965), which works when 2t+r<σ, where σ denotes the BCH-bound of the code     

Turbo encoding and decoding of Reed-Solomon codes through binary decomposition and self-concatenation

This paper presents a two-stage turbo-coding scheme for Reed-Solomon (RS) codes through binary decomposition and self-concatenation. In this scheme, the binary image of an RS code over GF(2/sup m/) is first decomposed into a set of binary component codes with relatively small trellis complexities. Then the RS code is formatted as a self-concatenated code with itself as the outer code and the binary component codes as the inner codes in a turbo-coding arrangement. In decoding, the inner codes are decoded with turbo decoding and the outer code is decoded with either an algebraic decoding algorithm or a reliability-based decoding algorithm. The outer and inner decoders interact during each decoding iteration. For RS codes of lengths up to 255, the proposed two-stage coding scheme is practically implementable and provides a significant coding gain over conventional algebraic and reliability-based decoding algorithms.     

A programmable decoder for Reed-Solomon codes

A programmable decoder for Reed-Solomon codes is described. The decoder is constructed using the Am2900 family of bit-slice elements and it is designed to perform error-correction erasure-filling decoding. The decoding rates obtained, lie in the range of 87 to 22 kilobits per second depending on the code and the error-erasure pattern encountered     

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.     

基于二分图的乘积码迭代译码算法

该文给出了由汉明分量乘积码构造广义低密度(GLD)码的一般方法。基于所得稀疏矩阵的二分图,并结合分组码与低密度校验(LDPC)码的译码算法,设计出一种新颖的可用于乘积码迭代译码的Chase-MP算法。由于所得二分图中不含有长度为4和6的小环,因而大大减少图上迭代时外信息之间的相关性,进而提高译码性能。对加性高斯白噪声(AWGN)及瑞利(Rayleigh)衰落信道下,汉明分量 (63,57,3)2 乘积码的模拟仿真显示,该算法能够获得很好的译码性能。与传统的串行迭代Chase-2算法相比,Chase-MP算法适合用于全并行译码处理,便于硬件实现,而且译码性能优于串行迭代Chase-2算法。     

On Diamond codes

A Diamond code is an error-correcting code obtained from two component codes. As in a product code, any symbol in a word of a Diamond code is checked by both component codes. However, the “code directions” for the component codes have been selected to minimize the memory that is required between successive decoding stages for the component codes. Diamond codes combine the error correcting power of a product code with the reduced memory requirements of the cross interleaved Reed-Solomon code (CIRC), applied in the compact disk system. We discuss encoding, decoding, and minimum distance properties of Diamond codes. Variations on the Diamond code construction are proposed that result in codes that are suited for use in rewritable block-oriented applications     

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.      

RAID6编码的扩展算法及性能研究

 RAID6编码根据其码字结构可以分为水平码和垂直码两大类.RAID6水平码可以很容易的扩展至任意码长,而RAID6垂直码通常具有码长的限制.本文提出一种针对RAID6垂直码的码长扩展算法,该算法通过校验块变更的方法,可以将RAID6垂直码扩展至任意码长.该算法可以保持RAID6垂直码的MDS特性.本文研究了RAID6编码的扩展算法在应用于各种RAID6编码时对其性能的影响,揭示出RAID6编码在进行扩展时的性能变化规律.     

Reed-Solomon codes for correcting phased error bursts

A code structure is introduced that represents a Reed-Solomon (RS) code in two-dimensional format. Based on this structure, a novel approach to multiple error burst correction using RS codes is proposed. For a model of phased error bursts, where each burst can affect one of the columns in a two-dimensional transmitted word, it is shown that the bursts can be corrected using a known multisequence shift-register synthesis algorithm. It is further shown that the resulting codes posses nearly optimal burst correction capability, under certain probability of decoding failure. Finally, low-complexity systematic encoding and syndrome computation algorithms for these codes are discussed. The proposed scheme may also find use in decoding of different coding schemes based on RS codes, such as product or concatenated codes.     

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     

Design and performance of turbo Gallager codes

The most powerful channel-coding schemes, namely, those based on turbo codes and low-density parity-check (LDPC) Gallager codes, have in common the principle of iterative decoding. However, the relative coding structures and decoding algorithms are substantially different. This paper shows that recently proposed novel coding structures bridge the gap between these two schemes. In fact, with properly chosen component convolutional codes, a turbo code can be successfully decoded by means of the decoding algorithm used for LDPC codes, i.e., the belief-propagation algorithm working on the code Tanner graph. These new turbo codes are here nicknamed "turbo Gallager codes." Besides being interesting from a conceptual viewpoint, these schemes are important on the practical side because they can be decoded in a fully parallel manner. In addition to the encoding complexity advantage of turbo codes, the low decoding complexity allows the design of very efficient channel-coding schemes.     

Product accumulate codes: a class of codes with near-capacity performance and low decoding complexity

We propose a novel class of provably good codes which are a serial concatenation of a single-parity-check (SPC)-based product code, an interleaver, and a rate-1 recursive convolutional code. The proposed codes, termed product accumulate (PA) codes, are linear time encodable and linear time decodable. We show that the product code by itself does not have a positive threshold, but a PA code can provide arbitrarily low bit-error rate (BER) under both maximum-likelihood (ML) decoding and iterative decoding. Two message-passing decoding algorithms are proposed and it is shown that a particular update schedule for these message-passing algorithms is equivalent to conventional turbo decoding of the serial concatenated code, but with significantly lower complexity. Tight upper bounds on the ML performance using Divsalar's (1999) simple bound and thresholds under density evolution (DE) show that these codes are capable of performance within a few tenths of a decibel away from the Shannon limit. Simulation results confirm these claims and show that these codes provide performance similar to turbo codes but with significantly less decoding complexity and with a lower error floor. Hence, we propose PA codes as a class of prospective codes with good performance, low decoding complexity, regular structure, and flexible rate adaptivity for all rates above 1/2.     

低重分配概率的OVSF码重分配算法

在采用正交可变长扩频因子(OVSF)码作为信道化码的直接序列扩频码分多址系统中,提出用重分配概率作为重分配算法的一个新的评价指标,重分配概率越小,系统的计算复杂度越低。进而提出一种低重分配概率的、基于空码容量的重分配算法,在解决本次码阻塞的同时,兼顾对未来高数据速率的呼叫的支持能力,减少未来码阻塞发生。仿真证实,重分配概率比已有2种重分配算法都小。     

Simple MAP decoding of first-order Reed-Muller and Hamming codes

A maximum a posteriori (MAP) probability decoder of a block code minimizes the probability of error for each transmitted symbol separately. The standard way of implementing MAP decoding of a linear code is the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm, which is based on a trellis representation of the code. The complexity of the BCJR algorithm for the first-order Reed-Muller (RM-1) codes and Hamming codes is proportional to n/sup 2/, where n is the code's length. In this correspondence, we present new MAP decoding algorithms for binary and nonbinary RM-1 and Hamming codes. The proposed algorithms have complexities proportional to q/sup 2/n log/sub q/n, where q is the alphabet size. In particular, for the binary codes this yields complexity of order n log n.     

The algebraic decoding of Goppa codes

An interesting class of linear error-correcting codes has been found by Goppa [3], [4]. This paper presents algebraic decoding algorithms for the Goppa codes. These algorithms are only a little more complex than Berlekamp's well-known algorithm for BCH codes and, in fact, make essential use of his procedure. Hence the cost of decoding a Goppa code is similar to the cost of decoding a BCH code of comparable block length.     

基于LDPC乘积码的BICM-ID方案性能分析

提出一种采用LDPC乘积码和BICM-ID相结合的编码调制技术.该方案编码采用LDPC乘积码,译码可以采取三个迭代过程:在解调器和译码器之间迭代,LDPC乘积码的分量码之间迭代,以及分量码内部迭代.因此采取合理的迭代译码策略,可以提高的译码效率.仿真结果显示,该方案在AWGN信道和Rayleigh信道条件下,与数字电视地面多媒体广播DTMB采用的编码调制方案相比具有更好的误比特性能.     

Near-optimum decoding of product codes: block turbo codes

This paper describes an iterative decoding algorithm for any product code built using linear block codes. It is based on soft-input/soft-output decoders for decoding the component codes so that near-optimum performance is obtained at each iteration. This soft-input/soft-output decoder is a Chase decoder which delivers soft outputs instead of binary decisions. The soft output of the decoder is an estimation of the log-likelihood ratio (LLR) of the binary decisions given by the Chase decoder. The theoretical justifications of this algorithm are developed and the method used for computing the soft output is fully described. The iterative decoding of product codes is also known as the block turbo code (BTC) because the concept is quite similar to turbo codes based on iterative decoding of concatenated recursive convolutional codes. The performance of different Bose-Chaudhuri-Hocquenghem (BCH)-BTCs are given for the Gaussian and the Rayleigh channel. Performance on the Gaussian channel indicates that data transmission at 0.8 dB of Shannon's limit or more than 98% (R/C>0.98) of channel capacity can be achieved with high-code-rate BTC using only four iterations. For the Rayleigh channel, the slope of the bit-error rate (BER) curve is as steep as for the Gaussian channel without using channel state information     

Efficient heuristic search algorithms for soft-decision decoding oflinear block codes

Efficient new algorithms are presented for maximum-likelihood and suboptimal soft-decision decoding algorithms for linear block codes. The first algorithm, MA*, improves the efficiency of the A* decoding algorithm, conducting the heuristic search through a code tree while exploiting code-specific properties. The second algorithm, H*, reduces search space by successively estimating the cost of the minimum-cost codeword with a fixed value at each of the most reliable and linearly independent components of the received message. The third algorithm, directed search, finds the codeword closest to the received vector by exploring a continuous search space. The strengths of these three algorithms are combined in a hybrid algorithm, applied to the (128,64), the (256,131), and the (256,139) binary-extended Bose-Chaudhuri-Hocquenghem (BCH) codes. Simulation results show that this hybrid algorithm can efficiently decode the (128,64) code for any signal-to-noise ratio, with near-optimal performance. Previously, no practical decoder could have decoded this code with such a performance for all ranges of signal-to-noise ratio     

Probabilistic construction of large constraint length trellis codesfor sequential decoding

Probabilistic algorithms are given for constructing good large constraint length trellis codes for use with sequential decoding that can achieve the channel cutoff rate bound at a bit error rate (BER) of 10^-5-10^-6. The algorithms are motivated by the random coding principle that an arbitrary selection of code symbols will produce a good code with high probability. One algorithm begins by choosing a relatively small set of codes randomly. The error performance of each of these codes is evaluated using sequential decoding and the code with the best performance among the chosen set is retained. Another algorithm treats the code construction as a combinatorial optimization problem and uses simulated annealing to direct the code search. Trellis codes for 8 PSK and 16 QAM constellations with constraint lengths v up to 20 are obtained. Simulation results with sequential decoding show that these codes reach the channel cutoff rate bound at a BER of 10^-5-10^-6 and achieve 5.0-6.35 dB real coding gains over uncoded systems with the same spectral efficiency and up to 2.0 dB real coding gains over 64 state trellis codes using Viterbi decoding