3 research outputs found
Efficient Maximum-Likelihood Decoding of Linear Block Codes on Binary Memoryless Channels
In this work, we consider efficient maximum-likelihood 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-the-art 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.Comment: Submitted to 2014 International Symposium on Information Theory. 5
Pages. Accepte
Introduction to Mathematical Programming-Based Error-Correction Decoding
Decoding error-correctiong codes by methods of mathematical optimization,
most importantly linear programming, has become an important alternative
approach to both algebraic and iterative decoding methods since its
introduction by Feldman et al. At first celebrated mainly for its analytical
powers, real-world applications of LP decoding are now within reach thanks to
most recent research. This document gives an elaborate introduction into both
mathematical optimization and coding theory as well as a review of the
contributions by which these two areas have found common ground.Comment: LaTeX sources maintained here: https://github.com/supermihi/lpdintr