Asymptotic optimality of the GMD and Chase decoding algorithms

The generalized minimum distance (GMD) and Chase (1972) decoding algorithms are some of the most important suboptimum bounded distance decoding algorithms for binary linear block codes over an additive white Gaussian noise (AWGN) channel. We compute the limitation of the ratio between the probability of decoding error for the GMD or any one of the Chase decoding algorithms and that of the maximum-likelihood (ML) decoding when the signal-to-noise ratio (SNR) approaches infinity. If the minimum Hamming distance of the code is greater than 2, the limitation is shown to be equal to 1 and thus the GMD and Chase decoding algorithms are asymptotically optimum.     

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     

Error performance analysis for reliability-based decodingalgorithms

The statistical approach proposed by Agrawal and Vardy (see ibid., vol.46, no.1, p.60-83, 2000) to evaluate the error performance of the generalized minimum distance (GMD) decoding is extended to other reliability-based decoding algorithms for binary linear block codes, namely Chase (1972) type, combined GMD and Chase type, and order statistic decoding (OSD). In all cases, tighter and simpler bounds than those previously proposed have been obtained with this approach     

Limited-trial Chase decoding

Chase decoders permit flexible use of reliability information in algebraic decoding algorithms for error-correcting block codes of Hamming distance d. The least complex version of the original Chase algorithms uses roughly d/2 trials of a conventional binary decoder, after which the best decoding result is selected as the final output. On certain channels, this approach achieves asymptotically the same performance as maximum-likelihood (ML) decoding. In this correspondence, the performance of Chase-like decoders with even less trials is studied. Most strikingly, it turns out that asymptotically optimal performance can be achieved by a version which uses only about d/4 trials.     

An efficient maximum-likelihood-decoding algorithm for linear blockcodes with algebraic decoder

A new soft decoding algorithm for linear block codes is proposed. The decoding algorithm works with any algebraic decoder and its performance is strictly the same as that of maximum-likelihood-decoding (MLD). Since our decoding algorithm generates sets of different candidate codewords corresponding to the received sequence, its decoding complexity depends on the received sequence. We compare our decoding algorithm with Chase (1972) algorithm 2 and the Tanaka-Kakigahara (1983) algorithm in which a similar method for generating candidate codewords is used. Computer simulation results indicate, for some signal-to-noise ratios (SNR), that our decoding algorithm requires less average complexity than those of the other two algorithms, but the performance of ours is always superior to those of the other two     

A CASCADABLE PRAGMATIC BLOCK DECODING ALGORITHM EXPLOITING CHANNEL MEASUREMENT INFORMATION

The complexity of algorithms to perform soft decision decoding on block codes has impeded their inclusion in practical systems. A well-known class of algorithms for decoding block codes utilizing channel measurement information along with the algebraic properties of the code are the Chase algorithms.¹ In this paper a decoding method similar to Chase's third algorithm is presented. However, in this method, a single test pattern or alternate codeword makes up one stage of the decoder. The method uses information from the previous decoding(s) to assist in generating a test pattern. This single stage ‘Second Chance Algorithm’ can then be extended to a ‘Third Chance Algorithm’ (and beyond) to enhance performance. The method does not invoke the hard decision decoder as often as the Chase algorithms.     

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.     

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.     

Adaptive Chase algorithm for block turbo codes

A new adaptive implementation of the Chase algorithm for binary block codes is proposed. Compared to the two efficiently used algorithms for iterative decoding, the standard Chase and Kaneko algorithms, the proposed algorithm can give the same performance with significant complexity reduction.     

Multiple-bases belief-propagation decoding of high-density cyclic codes

We introduce a new method for decoding short and moderate-length linear block codes with dense paritycheck matrix representations of cyclic form. This approach is termed multiple-bases belief-propagation. The proposed iterative scheme makes use of the fact that a code has many structurally diverse parity-check matrices, capable of detecting different error patterns. We show that this inherent code property leads to decoding algorithms with significantly better performance when compared to standard belief-propagation decoding. Furthermore, we describe how to choose sets of parity-check matrices of cyclic form amenable for multiple-bases decoding, based on analytical studies performed for the binary erasure channel. For several cyclic and extended cyclic codes, the multiple-bases belief propagation decoding performance can be shown to closely follow that of the maximum-likelihood decoder.     

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.     

On algebraic soft-decision decoding algorithms for BCH codes

Three algebraic soft-decision decoding algorithms are presented for binary Bose-Chaudhuri-Hocquengham (BCH) codes. Two of these algorithms are based on the bounded distance (BD)+1 generalized minimum-distance (GMD) decoding presented by Berlekamp (1984), and the other is based on Chase (1972) decoding. A simple algebraic algorithm is first introduced, and it forms a common basis for the decoding algorithms presented. Next, efficient BD+1 GMD and BD+2 GMD decoding algorithms are presented. It is shown that, for binary BCH codes with odd designed-minimum-distance d and length n, both the BD+1 GMD and the BD+2 GMD decoding algorithms can be performed with complexity O(nd). The error performance of these decoding algorithms is shown to be significantly superior to that of conventional GMD decoding by computer simulation. Finally, an efficient algorithm is presented for Chase decoding of binary BCH codes. Like a one-pass GMD decoding algorithm, this algorithm produces all necessary error-locator polynomials for Chase decoding in one run     

Chase Decoding of Linear Z4 Codes at Low to Moderate Rates

A free Z₄ code C may be decoded by decoding its canonical image C macr over Z₂ twice in succession. Hence, a Chase decoder for C could employ as its hard-decision (HD) decoder, a two-stage decoder which performs HD decoding on C macr in each stage. Alternatively, one could have a two-stage soft-decision decoder by employing a Chase decoder for C macr in each stage. We demonstrate that the latter approach can offer a significant reduction in complexity over the other, with little or no price to pay in terms of word error rate performance, particularly at low to moderate code rates.     

Generalized threshold chase algorithms

 Three generalized threshold Chase algorithms called GTC Ⅰ,GTC Ⅱ and STC are proposed in this paper.They are the combination of the generalized minimum distance(GMD)decoding algorithm with the Chase algorithm.Although the decoding error probabilities of these algorithms are very close to that of the Chase algorithm,the decoding speeds are faster,especially at higher signal-to-noise ratio(SNR),hence they are of greater practical value.The results of computer simulations are given,showing the advantages of these algorithms.     

An Efficient Stopping Criterion for Turbo Product Codes

In this letter, a stopping criterion using the error- detecting capability of linear block codes is proposed for the decoding of turbo product codes. The iterative decoding is stopped when the outputs from the Chase decoder are valid codewords for all rows and columns simultaneously. Simulation shows that the proposed method can reduce about one and half iterations compared with an existing stopping method, without noticeable BER performance loss. Some modification has also been discussed which may further reduce the decoding complexity.     

A Note on Limited-Trial Chase-Like Algorithms Achieving Bounded-Distance Decoding

For the decoding of a binary linear block code of minimal Hamming distance $d$ over additive white Gaussian noise (AWGN) channels, a soft-decision decoder achieves bounded-distance (BD) decoding if its squared error-correction radius is equal to $d$. A Chase-like algorithm outputs the best (most likely) codeword in a list of candidates generated by a conventional algebraic binary decoder in a few trials. It is of interest to design Chase-like algorithms that achieve BD decoding with as least trials as possible. In this paper, we show that Chase-like algorithms can achieve BD decoding with only $O(d^{1/2+varepsilon })$ trials for any given positive number $varepsilon $.      

5G LDPC码译码器实现

该文介绍了5G标准中LDPC码的特点,比较分析了各种译码算法的性能,提出了译码器实现的总体架构：将译码器分为高速译码器和低信噪比译码器。高速译码器适用于码率高、吞吐率要求高的情形,为译码器的主体;低信噪比译码器主要针对低码率、低信噪比下的高性能译码,处理一些极限情形下的通信,对吞吐率要求不高。分别对高速译码器和低信噪比译码器进行了设计实践,给出了FPGA综合结果和吞吐率分析结果。     

Using linear programming to Decode Binary linear codes

A new method is given for performing approximate maximum-likelihood (ML) decoding of an arbitrary binary linear code based on observations received from any discrete memoryless symmetric channel. The decoding algorithm is based on a linear programming (LP) relaxation that is defined by a factor graph or parity-check representation of the code. The resulting "LP decoder" generalizes our previous work on turbo-like codes. A precise combinatorial characterization of when the LP decoder succeeds is provided, based on pseudocodewords associated with the factor graph. Our definition of a pseudocodeword unifies other such notions known for iterative algorithms, including "stopping sets," "irreducible closed walks," "trellis cycles," "deviation sets," and "graph covers." The fractional distance d/sub frac/ of a code is introduced, which is a lower bound on the classical distance. It is shown that the efficient LP decoder will correct up to /spl lceil/d/sub frac//2/spl rceil/-1 errors and that there are codes with d/sub frac/=/spl Omega/(n/sup 1-/spl epsi//). An efficient algorithm to compute the fractional distance is presented. Experimental evidence shows a similar performance on low-density parity-check (LDPC) codes between LP decoding and the min-sum and sum-product algorithms. Methods for tightening the LP relaxation to improve performance are also provided.     

5G LDPC码译码器实现

该文介绍了5G标准中LDPC码的特点,比较分析了各种译码算法的性能,提出了译码器实现的总体架构：将译码器分为高速译码器和低信噪比译码器。高速译码器适用于码率高、吞吐率要求高的情形,为译码器的主体;低信噪比译码器主要针对低码率、低信噪比下的高性能译码,处理一些极限情形下的通信,对吞吐率要求不高。分别对高速译码器和低信噪比译码器进行了设计实践,给出了FPGA综合结果和吞吐率分析结果。     

三维TPC译码器的设计及FPGA实现

Turbo乘积码（TPC）是一种性能优秀的纠错编码方法,它具有译码复杂度低、译码延时小等优点,且在低信噪比下可以获得近似最优的性能。介绍了基于Chase算法的三维TPC软输入软输出（SISO）迭代译码算法,提出了三维TPC译码器硬件设计方案并在FPGA芯片上进行了仿真和验证。测试结果表明,该译码器具有较高的纠错能力,满足移动通信误码率的要求。