Geometrical and performance analysis of GMD and chase decoding algorithms

Eran Fishier, Ofer Amrani, Yair Be'ery

Research output: Contribution to journalArticlepeer-review

Abstract

The overall number of nearest neighbors in bounded distance decoding (BDD) algorithms is given by No,eff = No + NBDD, where NBDD denotes the number of additional, noncodeward, neighbors that are generated during the (suboptimal) decoding process. We identify and enumerate the nearest neighbors associated with the original Generalized Minimum Distance (GMD) and Chase decoding algorithms. After careful examination of the decision regions of these algorithms, we derive an approximated probability ratio between the error contribution of a noncodeword neighbor (one of NBDD points) and a codeword nearest neighbor. For Chase Algorithm 1 it is shown that the contribution to error probability of a noncodeword nearest neighbor is a factor of 2d-1 less than the contribution of a codeword, while for Chase Algorithm 2 the factor is 2[d/2]-1, d being the minimum Hamming distance of the code. For Chase Algorithm 3 and GMD, a recursive procedure for calculating this ratio, which turns out to be nonexponential in d, is presented. This procedure can also be used for specifically identifying the error patterns associated with Chase Algorithm 3 and GMD. Utilizing the probability ratio, we propose an improved approximated upper bound on the probability of error based on the union bound approach. Simulation results are given to demonstrate and support the analytical derivations.

Original languageEnglish
Pages (from-to)1406-1422
Number of pages17
JournalIEEE Transactions on Information Theory
Volume45
Issue number5
DOIs
StatePublished - 1999

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