The resolvability and the capacity of AWGN channels are equal

T. S. Han, S. Verdú

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

1 Scopus citations

Abstract

The authors have introduced the new notion of resolvability of a channel, as the dual of the capacity, which is defined as the minimum complexity per input letter needed to generate an input process whose output distribution via the channel arbitrarily accurately approximates any prescribed achievable output distribution. The resolvability thus introduced has revealed a deep relationship between the minimum achievable rate for source coding, the channel capacity, the identification capacity and the problem of random number generation. However, the validity of the proof of the converse for the resolvability formula established by Han and Verdu (see IEEE Trans. on Inform. Theory, vol.39, no.3, p.379, 1993) hinged heavily on the assumption that the input alphabet of the channel is finite. Our main purpose in this paper is to show that we can relax this restriction and to show that the resolvability formula of Han and Verdu continues to hold also for a wide class of channels with continuous input alphabet, including as a special case, additive white Gaussian noise (AWGN) channels with power constraint.

Original languageEnglish (US)
Title of host publicationProceedings - 1994 IEEE International Symposium on Information Theory, ISIT 1994
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages463
Number of pages1
ISBN (Print)0780320158, 9780780320154
DOIs
StatePublished - 1994
Event1994 IEEE International Symposium on Information Theory, ISIT 1994 - Trondheim, Norway
Duration: Jun 27 1994Jul 1 1994

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095

Other

Other1994 IEEE International Symposium on Information Theory, ISIT 1994
Country/TerritoryNorway
CityTrondheim
Period6/27/947/1/94

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Applied Mathematics
  • Modeling and Simulation

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