Jakov Snyders

Maximum likelihood soft decoding of binary block codes and decoders for the Golay codes

Maximum-likelihood soft-decision decoding of linear block codes is addressed. A binary multiple-check generalization of the Wagner rule is presented, and two methods for its implementation, one of which resembles the suboptimal Forney-Chase algorithms, are described. Besides efficient soft decoding of small codes, the generalized rule enables utilization of subspaces of a wide variety, thereby yielding maximum-likelihood decoders with substantially reduced computational complexity for some larger binary codes. More sophisticated choice and exploitation of the structure of both a subspace and the coset representatives are demonstrated for the (24, 12) Golay code, yielding a computational gain factor of about 2 with respect to previous methods. A ternary single-check version of the Wagner rule is applied for efficient soft decoding of the (12, 6) ternary Golay code. >

Reliability-based code-search algorithms for maximum-likelihood decoding of block codes

Efficient code-search maximum-likelihood decoding algorithms, based on reliability information, are presented for binary Linear block codes. The codewords examined are obtained via encoding. The information set utilized for encoding comprises the positions of those columns of a generator matrix G of the code which, for a given received sequence, constitute the most reliable basis for the column space of G. Substantially reduced computational complexity of decoding is achieved by exploiting the ordering of the positions within this information set. The search procedures do not require memory; the codeword to be examined is constructed from the previously examined codeword according to a fixed rule. Consequently, the search algorithms are applicable to codes of relatively large size. They are also conveniently modifiable to achieve efficient nearly optimum decoding of particularly large codes.

Optimal soft decision block decoders based on fast Hadamard transform

An approach for efficient utilization of fast Hadamard transform in decoding binary linear block codes is presented. Computational gain is obtained by employing various types of concurring codewords, and memory reduction is also achieved by appropriately selecting rows for the generator matrix. The availability of these codewords in general, and particularly in some of the most frequently encountered codes, is discussed.

Reduced lists of error patterns for maximum likelihood soft decoding

A method whereby a substantially reduced family of error patterns, called survivors, may be created for maximum likelihood soft decoding is introduced. The family of survivor depends on the received word. A decoder based on this approach first forms the survivors, then scores them. Rather than obtaining the survivors by online elimination of error patterns, the use of predetermined lists that represent all families of survivors is proposed. >

Fast decoding of the Leech lattice

An efficient algorithm is presented for maximum-likelihood soft-decision decoding of the Leech lattice. The superiority of this decoder with respect to both computational and memory complexities is demonstrated in comparison with previously published decoding methods. Gain factors in the range of 2-10 are achieved. The authors conclude with some more advanced ideas for achieving a further reduction of the algorithm complexity based on a generalization of the Wagner decoding method to two parity constraints. A comparison with the complexity of some trellis-coded modulation schemes is discussed. The decoding algorithm presented seems to achieve a computational complexity comparable to that of the equivalent trellis codes. >

Reliability-based syndrome decoding of linear block codes

In this correspondence, various aspects of reliability-based syndrome decoding of binary codes are investigated. First, it is shown that the least reliable basis (LRB) and the most reliable basis (MRB) are dual of each other. By exploiting this duality, an algorithm performing maximum-likelihood (ML) soft-decision syndrome decoding based on the LRB is presented. Contrarily to previous LRB-based ML syndrome decoding algorithms, this algorithm is more conveniently implementable for codes whose codimension is not small. New sufficient conditions for optimality are derived. These conditions exploit both the ordering associated with the LRB and the structure of the code considered. With respect to MRR-based sufficient conditions, they present the advantage of requiring no soft information and thus can be preprocessed and stored. Based on these conditions, low-complexity soft-decision syndrome decoding algorithms for particular classes of codes are proposed. Finally, a simple algorithm is analyzed. After the construction of the LRB, this algorithm computes the syndrome of smallest Hamming weight among o(K/sup i/) candidates, where K is the dimension of the code, for an order i of reprocessing. At practical bit-error rates, for codes of length N/spl les/128, this algorithm always outperforms any algebraic decoding algorithm capable of correcting up to t+1 errors with an order of reprocessing of at most 2, where t is the error-correcting capability of the code considered.

Concatenated multilevel block coded modulation

Encoding and decoding schemes for concatenated multilevel block codes are presented. By one of these structures, a real coding gain of 5.6-7.4 dB for the bit error range of 10/sup -6/ to 10/sup -9/ is achieved for transmission through the additive white Gaussian noise channel. Also, a rather large asymptotic coding gain is obtained. The new coding schemes have very low decoding complexity and increased coding gain in comparison with the conventional block and trellis coded modulation structures. A few design rules for concatenated (single and) multilevel block codes with large coding gain are also provided. >

Soft syndrome decoding of binary convolutional codes

We present an efficient recursive algorithm for accomplishing maximum likelihood (ML) soft syndrome decoding of binary convolutional codes. The algorithm consists of signal-by-signal hard decoding followed by a search for the most likely error sequence. The number of error sequences to be considered is substantially larger than in hard decoding, since the metric applied to the error bits is the magnitude of the log likelihood ratio rather than the Hamming weight. An error-trellis (alternatively, a decoding table) is employed for describing the recursion equations of the decoding procedure. The number of its states is determined by the states indicator, which is a modified version of the constraint length of the check matrix. Methods devised for eliminating error patterns and degenerating error-trellis sections enable accelerated ML decoding. In comparison with the Viterbi algorithm, the syndrome decoding algorithm achieves substantial reduction in the average computational complexity, particularly for moderately noisy channels. >

Error-trellises for convolutional codes .I. Construction

An error-trellis is a directed graph that represents all the sequences belonging to the coset which contains the symbol-by-symbol detected version of a given received sequence. A modular construction of error-trellises for an (n,k) convolutional code over GF(q) is presented. The trellis is designed on the basis of partitioning the scalar check matrix of the code into submatrices of l rows, accompanied with a corresponding segmentation of the syndrome. The value of the design parameter l is an essentially unconstrained multiple of n-k. For all the cosets of the code, the sections of the error-trellis are drawn from a collection of only q/sup l/ modules; the module for each section is determined by the value of the associated syndrome segment. In case the construction is based on a basic polynomial check matrix, either canonical or noncanonical, then the error-trellis is minimal in the sense that /spl sigma//spl les//spl mu/, where /spl sigma/ is the dimension of the state space of the trellis and /spl mu/ is the constraint length of a canonical generator matrix for the code. For basic check matrices with delay-free columns, the inequality reduces to /spl sigma/=/spl mu/.

Constrained designs for maximum likelihood soft decoding of RM(2,m) and the extended Golay codes

We present algorithms for maximum likelihood soft decoding of the second order Reed-Muller codes RM(2,m) and the extended (24,12,8) Golay code. The decoding procedures are based on a concise representation of appropriately selected cosets of a subcode of the code considered. Such representation enables application of certain elimination rules. A structure, called constrained design, is established to support the elimination procedures. Remarkably efficient algorithms are obtainable by this approach. The method of elimination is applicable also in combination with existing coset decoding schemes. >