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Information Theory and Applications Workshop (ITA)

On adaptive linear programming decoding of linear codes over GF(8)

Eirik Rosnes, Michael Helmling

Published Jan 1, 2016• 1 citation
Publisher

Abstract

In this work, we consider adaptive linear programming (LP) decoding of linear codes over GF(8). In particular, we give explicit constructions of valid inequalities (using no auxiliary variables) for the codeword polytope (or the convex hull) of the so-called constant-weight embedding of a single parity-check code over GF(8) that all are facet-defining. We conjecture that these inequalities together with so-called simplex constraints give a complete and irredundant description of the embedded (under the constant-weight embedding) codeword polytope. Furthermore, these sets of inequalities are used to develop an efficient (as compared to a static approach) exact (assuming that the conjecture is true) adaptive LP decoder for linear codes over GF(8). Numerical results show that only a very small subset of these inequalities is necessary for achieving close-to-exact LP decoding performance.

DOI
10.1109/ita.2016.7888132

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In this work, we consider adaptive linear programming (LP) decoding of linear codes over GF(8). In particular, we give explicit constructions of valid inequalities (using no auxiliary variables) for the codeword polytope (or the convex hull) of the so-called constant-weight embedding of a single parity-check code over GF(8) that all are facet-defining. We conjecture that these inequalities together with so-called simplex constraints give a complete and irredundant description of the embedded (under the constant-weight embedding) codeword polytope. Furthermore, these sets of inequalities are used to develop an efficient (as compared to a static approach) exact (assuming that the conjecture is true) adaptive LP decoder for linear codes over GF(8). Numerical results show that only a very small subset of these inequalities is necessary for achieving close-to-exact LP decoding performance.
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.
In this work, we consider adaptive linear programming (LP) decoding of ternary linear codes, i. e., linear codes over the finite field Fqwith 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 Fqwith q = 3melements, where m > 1 is a positive integer.
Abstract—Linear programming (LP) decoding approximates maximum-likelihood (ML) decoding of a linear block code by relaxing the equivalent ML integer programming (IP) problem into a more easily solved LP problem. The LP problem is defined by a set of box constraints together with a set of linear inequalities called “parity inequalities ” that are derived from the constraints represented by the rows of a parity-check matrix of the code and can be added iteratively and adaptively. In this paper, we first derive a new necessary condition and a new sufficient condition for a violated parity inequality constraint, or “cut, ” at a point in the unit hypercube. Then, we propose a new and effective algorithm to generate parity inequalities derived from certain additional redundant parity check (RPC) constraints that can eliminate pseudocodewords produced by the LP decoder, often significantly improving the decoder error-rate performance. The cut-generating algorithm is based upon a specific transformation of an initial parity-check matrix of the linear block code. We also design two variations of the proposed decoder to make it more efficient when it is combined with the new cut-generating algorithm. Simulation results for several low-density parity-check (LDPC) codes demonstrate that the proposed decoding algorithms significantly narrow the performance gap between LP decoding and ML decoding. Index Terms—Iterative decoding, linear codes, linear programming (LP) decoding, low-density parity-check (LDPC) codes, maximum-likelihood (ML) decoding, pseudocodewords. I.
Linear programming (LP) decoding approximates maximum-likelihood (ML) decoding of a linear block code by relaxing the equivalent ML integer programming (IP) problem into a more easily solved LP problem. The LP problem is defined by a set of box constraints together with a set of linear inequalities called “parity inequalities” that are derived from the constraints represented by the rows of a parity-check matrix of the code and can be added iteratively and adaptively. In this paper, we first derive a new necessary condition and a new sufficient condition for a violated parity inequality constraint, or “cut,” at a point in the unit hypercube. Then, we propose a new and effective algorithm to generate parity inequalities derived from certain additional redundant parity check (RPC) constraints that can eliminate pseudocodewords produced by the LP decoder, often significantly improving the decoder error-rate performance. The cut-generating algorithm is based upon a specific transformation of an initial parity-check matrix of the linear block code. We also design two variations of the proposed decoder to make it more efficient when it is combined with the new cut-generating algorithm. Simulation results for several low-density parity-check (LDPC) codes demonstrate that the proposed decoding algorithms significantly narrow the performance gap between LP decoding and ML decoding.
Linear programming (LP) decoding approximates optimal maximum-likelihood (ML) decoding of a linear block code by relaxing the equivalent ML integer programming (IP) problem into a more easily solved LP problem. The LP problem is defined by a set of linear inequalities derived from the constraints represented by the rows of a parity-check matrix of the code. Adaptive linear programming (ALP) decoding significantly reduces the complexity of LP decoding by iteratively and adaptively adding necessary constraints in a sequence of smaller LP problems. Adaptive introduction of constraints derived from certain additional redundant parity check (RPC) constraints can further improve ALP performance. In this paper, we propose a new and effective algorithm to identify RPCs that produce linear constraints, referred to as “cuts,” that can eliminate non-ML solutions generated by the ALP decoder, often significantly improving the decoder error-rate performance. The cut-finding algorithm is based upon a specific transformation of an initial parity-check matrix of the linear block code. Simulation results for several low-density parity-check codes demonstrate that the modified ALP decoding algorithm significantly narrows the performance gap between LP decoding and ML decoding.
Linear programming (LP) decoding was introduced by Feldman et al. (IEEE Trans. Inform. Theory Mar. 2005) as a novel way to decode binary low-density parity-check codes. Taghavi and Siegel (Proc. ISIT 2006) describe a computationally simplified decoding approach they term "adaptive" LP decoding. Adaptive LP decoding starts with a sub-set of the LP constraints, and iteratively adds violated constraints until an optimum of the original LP is found. Usually only a tiny fraction of the original constraints need to be reinstated, leading to huge efficiency gains compared to ordinary LP decoding. Here we describe a modification of the adaptive LP decoder that results in a maximum likelihood (ML) decoder. Whenever the adaptive LP decoder returns a pseudo-codeword rather than a codeword, we add an integer constraint on the least certain symbol of the pseudo-codeword. For certain codes, and especially in the high-SNR (error floor) regime, only a few integer constraints are required to force the resultant mixed-integer LP to the ML solution. We demonstrate that our approach can efficiently achieve the optimal ML decoding performance on a (155,64) LDPC code introduced by Tanner et al.