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IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences

Efficient Maximum Likelihood Decoding Algorithms for Linear Codes over Z-Channel (Special Section on Information Theory and Its Applications)

Tomohiko Uyematsu

Published Sep 25, 1993• 0 citations
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Maximum-likelihood decoding is often the optimal decoding rule one can use, but it is very costly to implement in a general setting. Much effort has therefore been dedicated to find efficient decoding algorithms that either achieve or approximate the error-correcting performance of the maximum-likelihood decoder. This dissertation examines two approaches to this problem. In 2003 Feldman and his collaborators defined the linear programming decoder, which operates by solving a linear programming relaxation of the maximum-likelihood decoding problem. As with many modern decoding algorithms, is possible for the linear programming decoder to output vectors that do not correspond to codewords; such vectors are known as pseudocodewords. In this work, we completely classify the set of linear programming pseudocodewords for the family of cycle codes. For the case of the binary symmetric channel, another approximation of maximum-likelihood decoding was introduced by Omura in 1972. This decoder employs an iterative algorithm whose behavior closely mimics that of the simplex algorithm. We generalize Omura's decoder to operate on any binary-input memoryless channel, thus obtaining a soft-decision decoding algorithm. Further, we prove that the probability of the generalized algorithm returning the maximum-likelihood codeword approaches 1 as the number of iterations goes to infinity.
In this paper we consider low-density parity-check (LDPC) codes with special construction. We obtain the lower-bounds on the error exponents for these codes under proposed low-complexity decoding algorithm and under a well known maximum likelihood decoding algorithm. We show that such LDPC code with special construction exists, that the error probability of the low-complexity decoding algorithm exponentially decreases with the code length for all code rates below the channel capacity. We also show that obtained lower-bound on the error exponent under the maximum likelihood decoding almost reaches the lower-bound on the error exponent of good linear codes under the maximum likelihood decoding. The error exponents are computed numerically for different code parameters.