Approximation Theory of Output Statistics

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Approximation Theory of Output Statistics (1993)

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by Te Sun Han , Sergio Verdg

Venue:	IEEE Trans. Inform. Theory
Citations:	171 - 11 self

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@ARTICLE{Han93approximationtheory,
    author = {Te Sun Han and Sergio Verdg},
    title = {Approximation Theory of Output Statistics},
    journal = {IEEE Trans. Inform. Theory},
    year = {1993},
    volume = {39},
    pages = {752--772}
}

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Abstract

Abstract-Given a channel and an input process, the minimum randomness of those input processes whose output statistics approximate the original output statistics with arbitrary accuracy is studied. The notion of resolvability of a channel, defined as the number of random bits required per channel use in order to generate an input that achieves arbitrarily accurate approximation of the output statistics for any given input process, is introduced. A general formula for resolvability that holds regardless of the channel memory structure, is obtained. It is shown that, for most channels, resolvability is equal to Shannon capacity. By-products of the analysis are a general formula for the minimum achievable (fixed-length) source coding rate of any finite-alphabet source, and a strong converse of the identification coding theorem, which holds for any channel that satisfies the strong converse of the channel coding theorem. Index Terms- Shannon theory, channel output statistics, re-solvability, random number generation complexity; channel ca-pacity, noiseless source coding theorem, identification via chan-nels. T

Keyphrases

output statistic input process approximation theory strong converse general formula finite-alphabet source channel use noiseless source arbitrary accuracy random number generation complexity shannon capacity accurate approximation original output statistic channel memory structure minimum randomness random bit index term shannon theory channel output statistic

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