Approximation theory of output statistics

Te Sun Han, Sergio Verdu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Given a channel and an input process we study the minimum randomness of those input processes whose output statistics approximate the original output statistics with arbitrary accuracy. We introduce 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. We obtain a general formula for resolvability which holds regardless of the channel memory structure. We show that, for most channels, resolvability is equal to Shannon capacity. By-products of our 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 1993 IEEE International Symposium on Information Theory
PublisherPubl by IEEE
Number of pages1
ISBN (Print)0780308786
StatePublished - Jan 1 1993
EventProceedings of the 1993 IEEE International Symposium on Information Theory - San Antonio, TX, USA
Duration: Jan 17 1993Jan 22 1993

Publication series

NameProceedings of the 1993 IEEE International Symposium on Information Theory

Other

OtherProceedings of the 1993 IEEE International Symposium on Information Theory
CitySan Antonio, TX, USA
Period1/17/931/22/93

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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