On the communication exponent of distributed testing for Gaussian correlations

Yuval Kochman, Ligong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This work addresses distributed binary hypothesis testing, where observations at two terminals are jointly Gaussian, each one standard, with two possible correlation coefficients. We assume that one of the terminals is colocated with the decision center, and focus on a single (Stein) error exponent. Rather than the traditional exponent that is defined with respect to the source blocklength, we assume the source data to be unlimited, and consider the error exponent as a function of the communication message length. We examine two different approaches, one by quantization and the other by sending the index of the maximum, and find them to yield the same exponent. We further find that binning improves upon both approaches in the same way. Finally we compare the obtained exponents to two upper bounds and determine the optimal exponent in some very special cases.

Original languageAmerican English
Title of host publication2020 IEEE Information Theory Workshop, ITW 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781728159621
DOIs
StatePublished - 2020
Event2020 IEEE Information Theory Workshop, ITW 2020 - Virtual, Riva del Garda, Italy
Duration: 11 Apr 202115 Apr 2021

Publication series

Name2020 IEEE Information Theory Workshop, ITW 2020

Conference

Conference2020 IEEE Information Theory Workshop, ITW 2020
Country/TerritoryItaly
CityVirtual, Riva del Garda
Period11/04/2115/04/21

Bibliographical note

Publisher Copyright:
©2021 IEEE

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