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 journalArticlepeer-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 languageEnglish (US)
Pages (from-to)1256-1264
Number of pages9
JournalIEEE Transactions on Information Theory
Volume43
Issue number4
DOIs
StatePublished - 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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