## A new upper bound on the ML decoding error probability of linear binary block codes in AWGN interference (2004)

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Venue: | IEEE TRANS. ON INFORMATION THEORY |

Citations: | 7 - 1 self |

### BibTeX

@ARTICLE{Yousefi04anew,

author = {Shahram Yousefi and Amir K. Khandani},

title = {A new upper bound on the ML decoding error probability of linear binary block codes in AWGN interference},

journal = {IEEE TRANS. ON INFORMATION THEORY},

year = {2004},

volume = {50},

pages = {3026--3036}

}

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### Abstract

Performance evaluation of Maximum-Likelihood (ML) soft-decision-decoded binary block codes is usually carried out using bounding techniques. Many tight upper bounds on the error probability of binary codes are based on the so-called Gallager’s First Bounding Technique (GFBT). The Tangential Sphere Bound (TSB) of Poltyrev which is believed for many years to offer the tightest bound developed for binary block codes is an example. Within the framework of the TSB and GFBT, we apply a new method referred to as the “Added-Hyper-Plane” (AHP) technique, to the decomposition of the error probability. This results in a bound developed upon the application of two stages of the GFBT with two different Gallager regions culminating to a tightened upper bound beyond the TSB. The proposed bound is simple and only requires the spectrum of the binary code.