A General Formula for Channel Capacity

Sergio Verdú; Te Sun Han

doi:10.1109/18.335960

A General Formula for Channel Capacity

Sergio Verdú, Te Sun Han

Research output: Contribution to journal › Article › peer-review

564 Scopus citations

Abstract

A formula for the capacity of arbitrary single-user channels without feedback (not necessarily information stable, stationary, etc.) is proved. Capacity is shown to equal the supremum, over all input processes, of the input-output inf - information rate defined as the liminf in probability of the normalized information density. The key to this result is a new converse approach based on a simple new lower bound on the error probability of m-ary hypothesis testa among equiprobable hypotheses. A necessary and sufficient condition for the validity of the strong converse is given, as well as general expressions for ε-capacity.

Original language	English (US)
Pages (from-to)	1147-1157
Number of pages	11
Journal	IEEE Transactions on Information Theory
Volume	40
Issue number	4
DOIs	https://doi.org/10.1109/18.335960
State	Published - Jul 1994

All Science Journal Classification (ASJC) codes

Information Systems
Computer Science Applications
Library and Information Sciences

Keywords

Shannon theory
channel capacity
channel coding theorem
channels with memory
strong converse

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10.1109/18.335960

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title = "A General Formula for Channel Capacity",

abstract = "A formula for the capacity of arbitrary single-user channels without feedback (not necessarily information stable, stationary, etc.) is proved. Capacity is shown to equal the supremum, over all input processes, of the input-output inf - information rate defined as the liminf in probability of the normalized information density. The key to this result is a new converse approach based on a simple new lower bound on the error probability of m-ary hypothesis testa among equiprobable hypotheses. A necessary and sufficient condition for the validity of the strong converse is given, as well as general expressions for ε-capacity.",

keywords = "Shannon theory, channel capacity, channel coding theorem, channels with memory, strong converse",

author = "Sergio Verd{\'u} and Han, {Te Sun}",

note = "Funding Information: Manuscript received December 15,1992, revised June 12,1993. This work was supported in part by the National Science Foundation under PYI Award ECSE-8857689 and by a grant from NEC. This paper was presented in part at the 1993 BEE workshop on Information Theory, Shizuoka, Japan, June 1993. S. Verdti is with the Department of Electrical Engineering, Princeton University, Princtton, NJ 08544. T. S. Han is with the Graduate School of Information Systems, University of E l e c t r o - C , Tokyo 182, Japan. FEE Log Number 9402452. In fact, it was shown by Hu 131 that information stability is essentially equivalent to the validity of formula (1.2).",

year = "1994",

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doi = "10.1109/18.335960",

language = "English (US)",

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AU - Verdú, Sergio

AU - Han, Te Sun

N1 - Funding Information: Manuscript received December 15,1992, revised June 12,1993. This work was supported in part by the National Science Foundation under PYI Award ECSE-8857689 and by a grant from NEC. This paper was presented in part at the 1993 BEE workshop on Information Theory, Shizuoka, Japan, June 1993. S. Verdti is with the Department of Electrical Engineering, Princeton University, Princtton, NJ 08544. T. S. Han is with the Graduate School of Information Systems, University of E l e c t r o - C , Tokyo 182, Japan. FEE Log Number 9402452. In fact, it was shown by Hu 131 that information stability is essentially equivalent to the validity of formula (1.2).

PY - 1994/7

Y1 - 1994/7

N2 - A formula for the capacity of arbitrary single-user channels without feedback (not necessarily information stable, stationary, etc.) is proved. Capacity is shown to equal the supremum, over all input processes, of the input-output inf - information rate defined as the liminf in probability of the normalized information density. The key to this result is a new converse approach based on a simple new lower bound on the error probability of m-ary hypothesis testa among equiprobable hypotheses. A necessary and sufficient condition for the validity of the strong converse is given, as well as general expressions for ε-capacity.

AB - A formula for the capacity of arbitrary single-user channels without feedback (not necessarily information stable, stationary, etc.) is proved. Capacity is shown to equal the supremum, over all input processes, of the input-output inf - information rate defined as the liminf in probability of the normalized information density. The key to this result is a new converse approach based on a simple new lower bound on the error probability of m-ary hypothesis testa among equiprobable hypotheses. A necessary and sufficient condition for the validity of the strong converse is given, as well as general expressions for ε-capacity.

KW - Shannon theory

KW - channel capacity

KW - channel coding theorem

KW - channels with memory

KW - strong converse

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A General Formula for Channel Capacity

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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