On decoding iterated codes

Algorithms to decode iterated codes when at least one of the "component codes" is majority decodable are given. The decoding algorithms allow the use of the decoders of the component codes and still make it possible to correct all error patterns guaranteed to be correctable by the minimum distance of the iterated code.     

The serial concatenation of rate-1 codes through uniform random interleavers

Until the analysis of repeat accumulate codes by Divsalar et al. (1998), few people would have guessed that simple rate-1 codes could play a crucial role in the construction of "good" binary codes. We construct "good" binary linear block codes at any rate r<1 by serially concatenating an arbitrary outer code of rate r with a large number of rate-1 inner codes through uniform random interleavers. We derive the average output weight enumerator (WE) for this ensemble in the limit as the number of inner codes goes to infinity. Using a probabilistic upper bound on the minimum distance, we prove that long codes from this ensemble will achieve the Gilbert-Varshamov (1952) bound with high probability. Numerical evaluation of the minimum distance shows that the asymptotic bound can be achieved with a small number of inner codes. In essence, this construction produces codes with good distance properties which are also compatible with iterative "turbo" style decoding. For selected codes, we also present bounds on the probability of maximum-likelihood decoding (MLD) error and simulation results for the probability of iterative decoding error.     

Construction explicite de bons codes longs*

The conventional criterion of minimum distance is shown to be irrelevant to long block codes. As a criterion better fitted to a code of large length n, we propose the cross-entropy of its normalized weight distribution with respect to the binomial distribution of an n-uple resulting from an independent equally distributed random choice of each of its symbols. The iterated product of parity-check codes appears as a means for designing long codes by decimation among the whole set of the n-uples which is good for this criterion although poor in terms of its minimum distance. Computed results and those from simulated decoding are presented and compared with theoretical limits. They are shown to be close to the channel cutoff rate R₀ .     

On the construction of a class of majority-logic decodable codes

The attractiveness of majority-logic decoding is its simple implementation. Several classes of majority-logic decodable block codes have been discovered for the past two decades. In this paper, a method of constructing a new class of majority-logic decodable block codes is presented. Each code in this class is formed by combining majority-logic decodable codes of shorter lengths. A procedure for orthogonalizing codes of this class is formulated. For each code, a lower bound on the number of correctable errors with majority-logic decoding is obtained. An upper bound on the number of orthogonalization steps for decoding each code is derived. Several majority-logic decodable codes that have more information digits than the Reed-Muller codes of the same length and the same minimum distance are found. Some results presented in this paper are extensions of the results of Lin and Weldon [11] and Gore [12] on the majority-logic decoding of direct product codes.     

Weight distribution and performance of the iterated product of single-parity-check codes

We consider the iterated product of binary single-parity-check (spc) codes. We show that their weight distribution asymptotically approaches that of random coding if the smallest code length in the product approaches infinity. According to a specific criterion, the best choice of the product parameters consists of taking all spc codes of equal length. Estimates of the weight distribution obtained by simulation show that even moderately long codes have a weight distribution close to that obtained in the average by random coding. We also discuss decoding of these codes by iterated replication decoding and report results of its simulation.     

Bounded minimum distance decoding of unit memory codes

We propose an algorithm for bounded minimum distance decoding of (partial) unit memory codes up to half the “designed” extended row distance. It makes use of a reduced trellis with the nodes found by bounded minimum distance decoding of the block codes used in the unit memory code. The results can be extended to general multimemory codes. The complexity of this algorithm is upper bounded by 2(d¯ ₁^r-2d_α) times the complexity of the bounded minimum distance decoder of the block codes in the unit memory code. Here d_α is the linear increase of the designed extended row distance d¯_i^r     

A New Linear Programming Approach to Decoding Linear Block Codes

In this paper, we propose a new linear programming formulation for the decoding of general linear block codes. Different from the original formulation given by Feldman, the number of total variables to characterize a parity-check constraint in our formulation is less than twice the degree of the corresponding check node. The equivalence between our new formulation and the original formulation is proven. The new formulation facilitates to characterize the structure of linear block codes, and leads to new decoding algorithms. In particular, we show that any fundamental polytope is simply the intersection of a group of the so-called minimum polytopes, and this simplified formulation allows us to formulate the problem of calculating the minimum Hamming distance of any linear block code as a simple linear integer programming problem with much less auxiliary variables. We then propose a branch-and-bound method to compute a lower bound to the minimum distance of any linear code by solving a corresponding linear integer programming problem. In addition, we prove that, for the family of single parity-check (SPC) product codes, the fractional distance and the pseudodistance are both equal to the minimum distance. Finally, we propose an efficient algorithm for decoding SPC product codes with low complexity and maximum-likelihood (ML) decoding performance.      

On acceptance criterion for efficient successiveerrors-and-erasures decoding of Reed-Solomon and BCH codes

We describe an efficient algorithm for successive errors-and-erasures decoding of BCH codes. The decoding algorithm consists of finding all necessary error locator polynomials and errata evaluator polynomials, choosing the most appropriate error locator polynomial and errata evaluator polynomial, using these two polynomials to compute a candidate codeword for the decoder output, and testing the candidate for optimality via an originally developed acceptance criterion. Even in the most stringent case possible, the acceptance criterion is only a little more stringent than Forney's (1966) criterion for generalised minimum distance (GMD) decoding. We present simulation results on the error performance of our decoding algorithm for binary antipodal signals over an AWGN channel and a Rayleigh fading channel. The number of calculations of elements in a finite field that are required by our algorithm is only slightly greater than that required by hard-decision decoding, while the error performance is almost as good as that achieved with GMD decoding. The presented algorithm is also applicable to efficient decoding of product RS codes     

Hardness of approximating the minimum distance of a linear code

We show that the minimum distance d of a linear code is not approximable to within any constant factor in random polynomial time (RP), unless nondeterministic polynomial time (NP) equals RP. We also show that the minimum distance is not approximable to within an additive error that is linear in the block length n of the code. Under the stronger assumption that NP is not contained in random quasi-polynomial time (RQP), we show that the minimum distance is not approximable to within the factor 2/sup log1-/spl epsi//(n), for any /spl epsi/>0. Our results hold for codes over any finite field, including binary codes. In the process, we show that it is hard to find approximately nearest codewords even if the number of errors exceeds the unique decoding radius d/2 by only an arbitrarily small fraction /spl epsi/d. We also prove the hardness of the nearest codeword problem for asymptotically good codes, provided the number of errors exceeds (2/3)d. Our results for the minimum distance problem strengthen (though using stronger assumptions) a previous result of Vardy (1997) who showed that the minimum distance cannot be computed exactly in deterministic polynomial time (P), unless P = NP. Our results are obtained by adapting proofs of analogous results for integer lattices due to Ajtai (1998) and Micciancio (see SIAM J. Computing, vol.30, no.6, p.2008-2035, 2001). A critical component in the adaptation is our use of linear codes that perform better than random (linear) codes.     

On the computation of the performance probabilities for block codeswith a bounded-distance decoding rule

When a block code is used on a discrete memoryless channel with an incomplete decoding rule that is based on a generalized distance, the probability of decoding failure, the probability of erroneous decoding, and the expected number of symbol decoding errors can be expressed in terms of the generalized weight enumerator polynomials of the code. For the symmetric erasure channel, numerically stable methods to compute these probabilities or expectations are proposed for binary codes whose distance distributions are known, and for linear maximum distance separable (MDS) codes. The method for linear MDS codes saves the computation of the weight distribution and yields upper bounds for the probability of erroneous decoding and for the symbol error rate by the cumulative binomial distribution. Numerical examples include a triple-error-correcting Bose-Chaudhuri-Hocquenghem (BCH) code of length 63 and a Reed-Solomon code of length 1023 and minimum distance 31     

New decoding algorithm for Reed - Muller codes (Corresp.)

We interpret Reed-Muller codes in terms of superimposition and present a new decoding algorithm for Reed-Muller codes. Before presenting this algorithm, we propose a decoding algorithm for a class of simple iterated codes (SI codes) that will play an important role in our new decoding algorithm. Finally, we compare our algorithm with the conventional algorithm for the cyclic Reed-Muller codes from the standpoint of decoding delay.     

Sample-based classification procedures related to empiric distributions

We present new classes of binary codes that are constructed on the basis of concatenated codes and product codes. We discuss the random-error-correction capabilities of these codes. Some examples of the codes for the correction of random errors are given which have at least as many codewords as the best codes previously known (to the authors) with the same minimum distance and same number of check symbols. The burst-error-correction capabilities of the codes are also discussed. Several examples of the codes for the correction of both random errors and burst errors are given. A decoding algorithm for the codes is also described.     

Codes for iterative decoding from partial geometries

This paper develops codes suitable for iterative decoding using the sum-product algorithm. By considering a large class of combinatorial structures, known as partial geometries, we are able to define classes of low-density parity-check (LDPC) codes, which include several previously known families of codes as special cases. The existing range of algebraic LDPC codes is limited, so the new families of codes obtained by generalizing to partial geometries significantly increase the range of choice of available code lengths and rates. We derive bounds on minimum distance, rank, and girth for all the codes from partial geometries, and present constructions and performance results for the classes of partial geometries which have not previously been proposed for use with iterative decoding. We show that these new codes can achieve improved error-correction performance over randomly constructed LDPC codes and, in some cases, achieve this with a significant decrease in decoding complexity.     

New classes of binary codes constructed on the basis of concatenated codes and product codes

We present new classes of binary codes that are constructed on the basis of concatenated codes and product codes. We discuss the random-error-correction capabilities of these codes. Some examples of the codes for the correction of random errors are given which have at least as many codewords as the best codes previously known (to the authors) with the same minimum distance and same number of check symbols. The burst-error-correction capabilities of the codes are also discussed. Several examples of the codes for the correction of both random errors and burst errors are given. A decoding algorithm for the codes is also described.     

Fast generalized minimum-distance decoding of algebraic-geometryand Reed-Solomon codes

Generalized minimum-distance (GMD) decoding is a standard soft-decoding method for block codes. We derive an efficient general GMD decoding scheme for linear block codes in the framework of error-correcting pairs. Special attention is paid to Reed-Solomon (RS) codes and one-point algebraic-geometry (AG) codes. For RS codes of length n and minimum Hamming distance d the GMD decoding complexity turns out to be in the order O(nd), where the complexity is counted as the number of multiplications in the field of concern. For AG codes the GMD decoding complexity is highly dependent on the curve in consideration. It is shown that we can find all relevant error-erasure-locating functions with complexity O(o₁nd), where o₁ is the size of the first nongap in the function space associated with the code. A full GMD decoding procedure for a one-point AG code can be performed with complexity O(dn²)     

Generalized minimum distance decoding algorithms forQary output channels (Corresp.)

A new condition for a generalized minimum distance decoder to guarantee correct decoding is developed. Based on this condition, decoding algorithms for block codes, product codes, and completely orthogonalizable codes onQary output channels are presented. The results of computer simulations comparing the performance of these decoding algorithms with several other soft-decision decoding algorithma are also presented.     

Free distance bounds for convolutional codes

The best asymptotic bounds presently known on free distance for convolutional codes are presented from a unified point of view. Upper and lower bounds for both time-varying and fixed codes are obtained. A comparison is made between bounds for nonsystematic and systematic codes which shows that more free distance is available with nonsystematic codes. This result is important when selecting codes for use with sequential or maximum-likelihood (Viterbi) decoding since the probability of decoding error is closely related to the free distance of the code. An ancillary result, used in proving the lower bound on free distance for time-varying nonsystematic codes, furnishes a generalization of two earlier bounds on the definite decoding minimum distance of convolutional codes.     

Coding for the gaussian channel: The promise of weighted-output decoding

We introduce the concept of weighted-output decoding, which enables us to perform many successive decodings without information loss. The design of a coding system for the additive white Gaussian channel can thus be viewed as a means for combining many simple codes. We illustrate this concept by the example of parity-check codes which are combined according to Elias' iterated product. Examination of the performance thus obtained leads us to criticize the conventional minimal distance criterion and to propose as a criterion a proximity measure of the weight distribution of the code with respect to the binomial one. According to this point of view, the iterated product of parity-check codes appears as a means of decimation of the set of n-tuples.     

Hard- and soft-decision decoding beyond the half minimum distance---An algorithm for linear codes (Corresp.)

A decoding algorithm for linear codes that uses the minimum weight words of the dual code as parity checks is defined. This algorithm is able to correct beyond the half minimum distance and has the capability of including soft-decision decoding. Results on applying this algorithm to quadratic residue (QR) codes, BCH codes, and the Golay codes (with and without soft-decision decoding) are presented.     

A new class of binary codes constructed on the basis of BCH codes (Corresp.)

In this correspondence, we present a new class of binary codes that are constructed on the basis of BCH codes. Some examples of these codes are given, having more codewords than the best codes previously known (to the authors) with the same minimum distance and number of check symbols. A decoding algorithm for the codes is also described.