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The Computer Journal
Article . 2018 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2015
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Article . 2019
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Error Correction by Structural Simplicity: Correcting Samplable Additive Errors

Authors: Kenji Yasunaga;

Error Correction by Structural Simplicity: Correcting Samplable Additive Errors

Abstract

Abstract This paper explores the possibilities and limitations of error correction by the structural simplicity of error mechanisms. Specifically, we consider channel models, called samplable additive channels, in which (i) errors are efficiently sampled without the knowledge of the coding scheme or the transmitted codeword; (ii) the entropy of the error distribution is bounded; and (iii) the number of errors introduced by the channel is unbounded. For the channels, several negative and positive results are provided. Assuming the existence of one-way functions, there are samplable additive errors of entropy nε for ε∈(0,1) that are pseudorandom, and thus not correctable by efficient coding schemes. It is shown that there is an oracle algorithm that induces a samplable distribution over {0,1}n of entropy m=ω(logn) that is not pseudorandom, but is uncorrectable by efficient schemes of rate less than 1−m/n−o(1). The results indicate that restricting error mechanisms to be efficiently samplable and not pseudorandom is insufficient for error correction. As positive results, some conditions are provided under which efficient error correction is possible.

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Keywords

FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT)

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selected citations
These citations are derived from selected sources.
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!
1
Average
Average
Average
Green
hybrid