An Improved Bound on the List Error Probability and List Distance Properties

List decoding of binary block codes for the additive white Gaussian noise (AWGN) channel is considered. The output of a list decoder is a list of the most likely codewords, that is, the signal points closest to the received signal in the Euclidean-metric sense. A decoding error occurs when the transmitted codeword is not on this list. It is shown that the list error probability is fully described by the so-called list configuration matrix, which is the Gram matrix obtained from the signal vectors forming the list. The worst case list configuration matrix determines the minimum list distance of the code, which is a generalization of the minimum distance to the case of list decoding. Some properties of the list configuration matrix are studied and their connections to the list distance are established. These results are further exploited to obtain a new upper bound on the list error probability, which is tighter than the previously known bounds. This bound is derived by combining the techniques for obtaining the tangential union bound with an improved bound on the error probability for a given list. The results are illustrated by examples.     

Composition bounds for channel block codes

The performance of Channel block codes for a general channel is studied by examining the relationship between the rate of a code, the joint composition of pairs of codewords, and the probability of decoding error. At fixed rate, lower bounds and upper bounds, both on minimum Bhattacharyya distance between codewords and on minimum equivocation distance between codewords, are derived. These bounds resemble, respectively, the Gilbert and the Elias bounds on the minimum Hamming distance between codewords. For a certain large class of channels, a lower bound on probability of decoding error for low-rate channel codes is derived as a consequence of the upper bound on Bhattacharyya distance. This bound is always asymptotically tight at zero rate. Further, for some channels, it is asymptotically tighter than the straight line bound at low rates. Also studied is the relationship between the bounds on codeword composition for arbitrary alphabets and the expurgated bound for arbitrary channels having zero error capacity equal to zero. In particular, it is shown that the expurgated reliability-rate function for blocks of letters is achieved by a product distribution whenever it is achieved by a block probability distribution with strictly positive components.     

The Information-Bit Error Rate for Block Codes

The relations between the word error probability and the decoding algorithms for block codes are reviewed. A simple approximation that does not depend upon the code weight structure or the decoding details is derived for the information-bit error rate in terms of the channel-symbol error probability.     

Generalized IRA Erasure Correcting Codes for Hybrid Iterative/Maximum Likelihood Decoding

The design of low-density parity-check (LDPC) codes under hybrid iterative / maximum likelihood decoding is addressed for the binary erasure channel (BEC). Specifically, we focus on generalized irregular repeat-accumulate (GeIRA) codes, which offer both efficient encoding and design flexibility. We show that properly designed GeIRA codes tightly approach the performance of an ideal maximum distance separable (MDS) code, even for short block sizes. For example, our (2048,1024) code reaches a codeword error rate of 10^-5 at channel erasure probability isin= 0.450, where an ideal (2048,1024) MDS code would reach the same error rate at isin = 0.453.     

The Augmented State Diagram and its Application to Convolutional and Turbo Codes

Convolutional block codes, which are commonly used as constituent codes in turbo code configurations, accept a block of information bits as input rather than a continuous stream of bits. In this paper, we propose a technique for the calculation of the transfer function of convolutional block codes, both punctured and nonpunctured. The novelty of our approach lies in the augmentation of the conventional state diagram, which allows the enumeration of all codeword sequences of a convolutional block code. In the case of a turbo code, we can readily calculate an upper bound to its bit error rate performance if the transfer function of each constituent convolutional block code has been obtained. The bound gives an accurate estimate of the error floor of the turbo code and, consequently, our method provides a useful analytical tool for determining constituent codes or identifying puncturing patterns that improve the bit error rate performance of a turbo code, at high signal-to-noise ratios.     

An Improved Sphere-Packing Bound for Finite-Length Codes Over Symmetric Memoryless Channels

This paper derives an improved sphere-packing (ISP) bound for finite-length error-correcting codes whose transmission takes place over symmetric memoryless channels, and the codes are decoded with an arbitrary list decoder. We first review classical results, i.e., the 1959 sphere-packing (SP59) bound of Shannon for the Gaussian channel, and the 1967 sphere-packing (SP67) bound of Shannon et al. for discrete memoryless channels. An improvement on the SP67 bound, as suggested by Valembois and Fossorier, is also discussed. These concepts are used for the derivation of a new lower bound on the error probability of list decoding (referred to as the ISP bound) which is uniformly tighter than the SP67 bound and its improved version. The ISP bound is applicable to symmetric memoryless channels, and some of its applications are presented. Its tightness under maximum-likelihood (ML) decoding is studied by comparing the ISP bound to previously reported upper and lower bounds on the ML decoding error probability, and also to computer simulations of iteratively decoded turbo-like codes. This paper also presents a technique which performs the entire calculation of the SP59 bound in the logarithmic domain, thus facilitating the exact calculation of this bound for moderate to large block lengths without the need for the asymptotic approximations provided by Shannon.     

Structured set partitions and multilevel concatenated coding forpartial response channels

We present a systematic way to construct multilevel concatenated codes for partial response (PR) channels using: (1) a structured set partition (SSP) of multiple channel output sets and (2) a set of conventional block codes with different error correcting capabilities. A lower bound on the minimum squared Euclidean distance of the constructed codes is given. This bound is based on the interset minimal Euclidean distances of the SSP and the minimum Hamming distances of the used block codes. An example of SSP for the extended class 4 partial response channel (EPR4) is presented. Iterative suboptimal decoding, which combines Viterbi detection on the trellis of the PR channel with algebraic error detection/correction, can be applied to the constructed concatenated codes. Truncated versions of the iterative decoding scheme are simulated and compared with each other     

An Efficient Pseudocodeword Search Algorithm for Linear Programming Decoding of LDPC Codes

In linear programming (LP) decoding of a low-density parity-check (LDPC) code one minimizes a linear functional, with coefficients related to log-likelihood ratios, over a relaxation of the polytope spanned by the codewords. In order to quantify LP decoding it is important to study vertexes of the relaxed polytope, so-called pseudocodewords. We propose a technique to heuristcally create a list of pseudocodewords close to the zero codeword and their distances. Our pseudocodeword-search algorithm starts by randomly choosing configuration of the noise. The configuration is modified through a discrete number of steps. Each step consists of two substeps: one applies an LP decoder to the noise-configuration deriving a pseudocodeword, and then finds configuration of the noise equidistant from the pseudocodeword and the zero codeword. The resulting noise configuration is used as an entry for the next step. The iterations converge rapidly to a pseudocodeword neighboring the zero codeword. Repeated many times, this procedure is characterized by the distribution function of the pseudocodeword effective distance. The efficiency of the procedure is demonstrated on examples of the Tanner code and Margulis codes operating over an additive white Gaussian noise (AWGN) channel.     

基于分段凿孔的极化码级联方案

极化码拥有出色的纠错性能,但编码方式决定了其码长不够灵活,需要通过凿孔构造码长可变的极化码。该文引入矩阵极化率来衡量凿孔对极化码性能的影响,选择矩阵极化率最大的码字作为最佳凿孔模式。对极化码的码字进行分段,有效减小了最佳凿孔模式的搜索运算量。由于各分段的第1个码字都会被凿除,且串行抵消译码过程中主要发生1位错,因此在各段段首级联奇偶校验码作为译码提前终止标志,检测前段码字的译码错误并进行重新译码。对所提方法在串行抵消译码下的性能进行仿真分析,结果表明,相比传统凿孔方法,所提方法在10–3误码率时能获得约0.7 dB的编码增益,有效提升了凿孔极化码的译码性能。     

Waterfall performance analysis of finite-length LDPC codes on symmetric channels

An efficient method for analyzing the performance of finite-length low-density parity-check (LDPC) codes in the waterfall region, when transmission takes place on a memoryless binary-input output-symmetric channel is proposed. This method is based on studying the variations of the channel quality around its expected value when observed during the transmission of a finite-length codeword. We model these variations with a single parameter. This parameter is then viewed as a random variable and its probability distribution function is obtained. Assuming that a decoding failure is the result of an observed channel worse than the code?s decoding threshold, the block error probability of finite-length LDPC codes under different decoding algorithms is estimated. Using an extrinsic information transfer chart analysis, the bit error probability is obtained from the block error probability. Different parameters can be used for modeling the channel variations. In this work, two of such parameters are studied. Through examples, it is shown that this method can closely predict the performance of LDPC codes of a few thousand bits or longer in the waterfall region.     

Improved bounds on the word error probability of RA(2) codes with linear-programming-based decoding

This paper deals with the linear-programming-based decoding algorithm of Feldman and Karger for repeat-accumulate "turbo-like" codes. We present a new structural characterization that captures the event that decoding fails. Based on this structural characterization, we develop polynomial algorithms that, given an RA(2) code, compute upper and lower bounds on the word error probability P/sub w/ for the binary-symmetric and the additive white Gaussian noise (AWGN) channels. Our experiments with an implementation of these algorithms for bounding P/sub w/ demonstrate in many interesting cases an improvement in the upper bound on the word error probability by a factor of over 1000 compared to the bounds by Feldman et al.. The experiments also indicate that the improvement in upper bound increases as the codeword length increases and the channel noise decreases. The computed lower bounds on the word error probability in our experiments are roughly ten times smaller than the upper bound.     

Error probability and free distance bounds for two-user tree codes on multiple-access channels

Two-user tree codes are considered for use on an arbitrary two-user discrete memoryless multiple-access channel (MAC). A two-user tree Is employed to achieve true maximum likelihood (ML) decoding of two-user tree codes on MAC's. Each decoding error event has associated with it a configuration indicating the specific time slots in which a decoding error has occurred for the first user alone, for the second user alone, or for both users simultaneously. Even though there are many possible configurations, it is shown that there are five fundamental configuration types. An upper bound on decoding error probability, similar to Liao's result for two-user block codes, is derived for sets of error events having a particular configuration. The total ML decoding error probability is bounded using a union bound first over all configurations of a given type and then over the five configuration types. A two-user tree coding error exponent is defined and compared with the corresponding block coding result for a specific MAC. It is seen that the tree coding error exponent is larger than the block coding error exponent at all rate pairs within the two-user capacity region. Finally, a new lower bound on free distance for two-user codes is derived using the same general technique used to bound the error probability.     

SNR-Invariant Importance Sampling for Hard-Decision Decoding Performance of Linear Block Codes

We present an importance sampling (IS) technique for evaluating the word-error rate (WER) and bit-error rate (BER) performance of binary linear block codes under hard-decision decoding. This IS technique takes advantage of the invariance of the decoding outcome to the transition probability of the binary symmetric channel given a received error pattern, and is equivalent to the method of stratification for variance reduction. A thorough analysis of the accuracy of the proposed signal-to-noise-ratio-invariant IS (IIS) estimator based on computing its relative bias and standard deviation is provided. Under certain conditions, which may be achieved fairly easily for certain code and decoder combinations, we demonstrate that it is possible to use the proposed IIS technique to accurately evaluate the WER and BER to arbitrarily low values. Further, in all cases, the probability estimates obtained via IIS always serve as a lower bound on the true probability values     

A comparison of known codes, random codes, and the best codes

This paper calculates new bounds on the size of the performance gap between random codes and the best possible codes. The first result shows that, for large block sizes, the ratio of the error probability of a random code to the sphere-packing lower bound on the error probability of every code on the binary symmetric channel (BSC) is small for a wide range of useful crossover probabilities. Thus even far from capacity, random codes have nearly the same error performance as the best possible long codes. The paper also demonstrates that a small reduction k-k˜ in the number of information bits conveyed by a codeword will make the error performance of an (n,k˜) random code better than the sphere-packing lower bound for an (n,k) code as long as the channel crossover probability is somewhat greater than a critical probability. For example, the sphere-packing lower bound for a long (n,k), rate 1/2, code will exceed the error probability of an (n,k˜) random code if k-k˜>10 and the crossover probability is between 0.035 and 0.11=H^-1(1/2). Analogous results are presented for the binary erasure channel (BEC) and the additive white Gaussian noise (AWGN) channel. The paper also presents substantial numerical evaluation of the performance of random codes and existing standard lower bounds for the BEC, BSC, and the AWGN channel. These last results provide a useful standard against which to measure many popular codes including turbo codes, e.g., there exist turbo codes that perform within 0.6 dB of the bounds over a wide range of block lengths     

Automatic design of orthogonal or near-orthogonal linear dispersive space-time block codes

This paper considers a wireless multiple-input multiple-output (MIMO) communication system in a frequency-nonselective scenario with spatially uncorrelated Rayleigh fading channel coefficients and investigates the design of linear dispersive (LD) space-time block codes. Efficient LD codes are obtained by optimizing the constituent weight matrices so that an upper bound on the union bound of the codeword error probability is minimized. Interestingly, the proposed design procedure automatically generates LD codes that either correspond to, or are close to, the well-known class of orthogonal space-time block (OSTB) codes. A theoretical analysis confirms this by proving that OSTB codes are indeed optimal, when the setup under study permits their existence. Simulation results demonstrate the excellent performance of the designed codes. In particular, the importance of the codes' near-orthogonal property is illustrated by showing that low-complexity linear equalizer techniques can be used for decoding purposes while incurring a relatively moderate performance loss compared with optimal maximum-likelihood (ML) decoding.     

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.      

Theoretical analysis of the error correction performance ofmajority-logic-like vector symbol codes

The average codeword success probability of the majority-logic-like vector symbol (MLLVS) code is derived for the following two cases: (1) single-pass decoding and (2) upper bound of multipass decoding, when the received word has more than (J-1) symbol errors, where J is the number of check sum equations. The MLLVS code has been simulated by Metzner (1996), and it was concluded that the average error correcting capability of MLLVS codes exceed the decoding capability of Reed-Solomon codes, but is achieved with less complexity. Additionally, for codes that have larger structures, the error correcting capability is sustained even further with a high probability of decoding success through multipass decoding procedures. The mathematical derivations of the error correction performance beyond (J-1) symbol errors serve as theoretical proof of the MLLVS code error correcting capability that was shown only through simulation results until now by Metzner. One characteristic feature of this derivation is that it does not assume any specific inner code usage, enabling the derived decoding probability equations to be easily applied to any inner code selected, of a concatenated coding structure     

Lower bounds on the error probability of block codes based on improvements on de Caen's inequality

New lower bounds on the error probability of block codes with maximum-likelihood decoding are proposed. The bounds are obtained by applying a new lower bound on the probability of a union of events, derived by improving on de Caen's lower bound. The new bound includes an arbitrary function to be optimized in order to achieve the tightest results. Since the optimal choice of this function is known, but leads to a trivial and useless identity, we find several useful approximations for it, each resulting in a new lower bound. For the additive white Gaussian noise (AWGN) channel and the binary-symmetric channel (BSC), the optimal choice of the optimization function is stated and several approximations are proposed. When the bounds are further specialized to linear codes, the only knowledge on the code used is its weight enumeration. The results are shown to be tighter than the latest bounds in the current literature, such as those by Seguin (1998) and by Keren and Litsyn (2001). Moreover, for the BSC, the new bounds widen the range of rates for which the union bound analysis applies, thus improving on the bound to the error exponent compared with the de Caen-based bounds.     

基于分布式奇偶校验码的低复杂度极化码SCLF译码算法

针对极化码串行抵消列表比特翻转(Successive Cancellation List Bit-Flip, SCLF)译码算法复杂度较高的问题,提出一种基于分布式奇偶校验码的低复杂度极化码SCLF译码(SCLF Decoding Algorithm for Low-Complexity Polar Codes Based on Distributed Parity Check Codes, DPC-SCLF)算法。与仅采用循环冗余校验(Cyclic Redundancy Check, CRC)码校验的SCLF译码算法不同,该算法首先利用极化信道偏序关系构造关键集,然后采用分布式奇偶校验(Parity Check, PC)码与CRC码结合的方式对错误比特进行检验、识别和翻转,提高了翻转精度,减少了重译码次数。此外,在译码时利用路径剪枝操作,提高了正确路径的竞争力,改善了误码性能,且利用提前终止译码进程操作,减少了译码比特数。仿真结果表明,与D-Post-SCLF译码算法和RCS-SCLF译码算法相比,所提出算法具有更低的译码复杂度且在中高信噪比下具有更好的误码性能。     

Performance evaluation of low-density parity-check codes on partial-response channels using density evolution

A method to evaluate the performance of a low-density parity-check (LDPC) code on partial-response (PR) channels in terms of the noise threshold and decoding error is presented. Given a particular codeword or assuming an independent and uniformly distributed (i.u.d.) codeword for transmission, the density-evolution algorithm is used to compute the probability density function of messages passing in the decoding process, from which the decoding error is extracted. This estimated i.u.d. decoding error is used to approximate the decoding error of an ensemble of LDPC codes on arbitrary PR channels. Comparison with simulation results shows that it is a very good approximation for the simulated codes, provided their length is large enough.