Abstract-Mathematical programming is a branch of applied mathematics and has recently been used to derive new decoding approaches, challenging established but often heuristic algorithms based on iterative message passing. Concepts from mathematical programming used in the context of decoding include linear, integer, and nonlinear programming, network flows, notions of duality as well as matroid and polyhedral theory. This survey article reviews and categorizes decoding methods based on mathematical programming approaches for binary linear codes over binary-input memoryless symmetric channels.
Abstract-In this work, we consider efficient maximumlikelihood decoding of linear block codes for small-to-moderate block lengths. The presented approach is a branch-and-bound algorithm using the cutting-plane approach of Zhang and Siegel (IEEE Trans. Inf. Theory, 2012) for obtaining lower bounds. We have compared our proposed algorithm to the state-of-theart commercial integer program solver CPLEX, and for all considered codes our approach is faster for both low and high signal-to-noise ratios. For instance, for the benchmark (155, 64) Tanner code our algorithm is more than 11 times as fast as CPLEX for an SNR of 1.0 dB on the additive white Gaussian noise channel. By a small modification, our algorithm can be used to calculate the minimum distance, which we have again verified to be much faster than using the CPLEX solver.
In this work, we consider adaptive linear programming (LP) decoding of ternary linear codes, i. e., linear codes over the finite field Fq with q = 3 elements. In particular, we characterize completely the codeword polytope (or the convex hull) of the binary image, under Flanagan's embedding, of a ternary single parity-check code. Then, this characterization is used to develop an efficient adaptive LP decoder for ternary codes. Numerical experiments confirm that this decoder is very efficient compared to a static LP decoder and scales well with both block length and check node degree. Finally, we briefly consider the case of nonbinary codes over the finite field Fq with q = 3 m elements, where m > 1 is a positive integer.
In this work, we study linear programming (LP) decoding of nonbinary linear codes over prime fields. In particular, we develop a novel separation algorithm for valid inequalities describing the codeword polytope of the so-called constant-weight embedding of a single parity-check (SPC) code over any prime field. The algorithm has linear (in the length of the SPC code) complexity, is structurally different from the one for binary codes, and is based on the principle of dynamic programming. Furthermore, it is the basis of the proposed efficient (relaxed) adaptive LP (ALP) decoder for general (non-SPC) linear codes over any prime field, generalizing the well-known ALP decoding algorithm for binary codes. Numerical results show that the ALP decoding algorithm is very efficient compared to a static approach
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