Worst case additive noise for binary-input channels and zero-threshold detection under constraints of power and divergence

Andrew L. McKellips; Sergio Verdú

doi:10.1109/18.605590

Worst case additive noise for binary-input channels and zero-threshold detection under constraints of power and divergence

Andrew L. McKellips, Sergio Verdú

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

13 Scopus citations

Abstract

Additive-noise channels with binary inputs and zero-threshold detection are considered. We study worst case noise under the criterion of maximum error probability with constraints on both power and divergence with respect to a given symmetric nominal noise distribution. Particular attention is focused on the cases of a) Gaussian nominal distributions and b) asymptotic increase in worst case error probability when the divergence tolerance tends to zero.

Original language	English (US)
Pages (from-to)	1256-1264
Number of pages	9
Journal	IEEE Transactions on Information Theory
Volume	43
Issue number	4
DOIs	https://doi.org/10.1109/18.605590
State	Published - 1997

All Science Journal Classification (ASJC) codes

Information Systems
Computer Science Applications
Library and Information Sciences

Keywords

Detection
Gaussian error probability
Hypothesis testing
Kullback-Leibler divergence
Least favorable noise

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10.1109/18.605590

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title = "Worst case additive noise for binary-input channels and zero-threshold detection under constraints of power and divergence",

abstract = "Additive-noise channels with binary inputs and zero-threshold detection are considered. We study worst case noise under the criterion of maximum error probability with constraints on both power and divergence with respect to a given symmetric nominal noise distribution. Particular attention is focused on the cases of a) Gaussian nominal distributions and b) asymptotic increase in worst case error probability when the divergence tolerance tends to zero.",

keywords = "Detection, Gaussian error probability, Hypothesis testing, Kullback-Leibler divergence, Least favorable noise",

author = "McKellips, {Andrew L.} and Sergio Verd{\'u}",

note = "Funding Information: Manuscript received November 3, 1995; revised December 23, 1996. This work was supported in part by the U.S. Army Research Office under Grant DAAH04-96-1-0379. The mateial in this correspondence was presented in part at the 33rd Annual Allerton Conference on Communication, Control and Computing, Monticello, IL, October 1995.",

year = "1997",

doi = "10.1109/18.605590",

language = "English (US)",

volume = "43",

pages = "1256--1264",

journal = "IEEE Transactions on Information Theory",

issn = "0018-9448",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - Worst case additive noise for binary-input channels and zero-threshold detection under constraints of power and divergence

AU - McKellips, Andrew L.

AU - Verdú, Sergio

N1 - Funding Information: Manuscript received November 3, 1995; revised December 23, 1996. This work was supported in part by the U.S. Army Research Office under Grant DAAH04-96-1-0379. The mateial in this correspondence was presented in part at the 33rd Annual Allerton Conference on Communication, Control and Computing, Monticello, IL, October 1995.

PY - 1997

Y1 - 1997

N2 - Additive-noise channels with binary inputs and zero-threshold detection are considered. We study worst case noise under the criterion of maximum error probability with constraints on both power and divergence with respect to a given symmetric nominal noise distribution. Particular attention is focused on the cases of a) Gaussian nominal distributions and b) asymptotic increase in worst case error probability when the divergence tolerance tends to zero.

AB - Additive-noise channels with binary inputs and zero-threshold detection are considered. We study worst case noise under the criterion of maximum error probability with constraints on both power and divergence with respect to a given symmetric nominal noise distribution. Particular attention is focused on the cases of a) Gaussian nominal distributions and b) asymptotic increase in worst case error probability when the divergence tolerance tends to zero.

KW - Detection

KW - Gaussian error probability

KW - Hypothesis testing

KW - Kullback-Leibler divergence

KW - Least favorable noise

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JO - IEEE Transactions on Information Theory

JF - IEEE Transactions on Information Theory

IS - 4

ER -

Worst case additive noise for binary-input channels and zero-threshold detection under constraints of power and divergence

Abstract

All Science Journal Classification (ASJC) codes

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