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Improved Hamming sphere bounds on the MLD performance of binary linear codes

Authors: Xiao Ma; Jia Liu;

Improved Hamming sphere bounds on the MLD performance of binary linear codes

Abstract

In this paper, improved Hamming sphere bounds on the maximum-likelihood decoding (MLD) performance of binary linear codes over additive white Gaussian noise (AWGN) channels are proposed. The proposed Hamming sphere bounds both on the frame and bit error probabilities are based on Gallager's first bounding technique (GFBT), where the “good” region is chosen to be an Hamming sphere, which is different from the conventional definition by the Euclidean distance. The good region is then divided into small regions to tighten the union bound on the error probability caused by the Hamming sphere. The proposed bounds require only the knowledge of the truncated weight spectrum of the code, which is helpful when the whole weight spectrum is unknown or not computable. Numerical results show that the proposed bounds are tighter than most of the upper bounds and even tighter than the tangential-sphere bound (TSB) for high code rates.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Average
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