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Implicit channel estimation for ML sequence detection over finite-state Markov communication channels

Krusevac, Zarko; Kennedy, Rodney; Rapajic, Predrag

Description

This paper shows the existence of the optimal training, in terms of achievable mutual information rate, for an output feedback implicit estimator for finite-state Markov communication channels. Implicit (blind) estimation is based on a measure of how modified is the input distribution when filtered by the channel transfer function and it is shown that there is no modification of an input distribution with maximum entropy rate. Input signal entropy rate reduction enables implicit (blind) channel...[Show more]

dc.contributor.authorKrusevac, Zarko
dc.contributor.authorKennedy, Rodney
dc.contributor.authorRapajic, Predrag
dc.coverage.spatialPerth Australia
dc.date.accessioned2015-12-08T22:13:50Z
dc.date.available2015-12-08T22:13:50Z
dc.date.createdFebruary 1-3 2006
dc.identifier.isbn1424402131
dc.identifier.urihttp://hdl.handle.net/1885/29979
dc.description.abstractThis paper shows the existence of the optimal training, in terms of achievable mutual information rate, for an output feedback implicit estimator for finite-state Markov communication channels. Implicit (blind) estimation is based on a measure of how modified is the input distribution when filtered by the channel transfer function and it is shown that there is no modification of an input distribution with maximum entropy rate. Input signal entropy rate reduction enables implicit (blind) channel process estimation, but decreases information transmission rate. The optimal input entropy rate (optimal implicit training rate) which achieves the maximum mutual information rate, is found.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesAustralian Communications Theory Workshop (AusCTW 2006)
dc.sourceProceedings of the 7th Australian Communications Theory Workshop
dc.source.urihttp://ausctw06.watri.org.au
dc.subjectKeywords: Finite element method; Markov processes; Parameter estimation; Signal processing; Transfer functions; Feedback implicit estimators; Markov communication channels; Maximum entropy rate; Communication systems
dc.titleImplicit channel estimation for ML sequence detection over finite-state Markov communication channels
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2006
local.identifier.absfor100510 - Wireless Communications
local.identifier.absfor090609 - Signal Processing
local.identifier.ariespublicationu3357961xPUB70
local.type.statusPublished Version
local.contributor.affiliationKrusevac, Zarko, College of Engineering and Computer Science, ANU
local.contributor.affiliationKennedy, Rodney, College of Engineering and Computer Science, ANU
local.contributor.affiliationRapajic, Predrag , University of Greenwich
local.bibliographicCitation.startpage128
local.bibliographicCitation.lastpage134
dc.date.updated2015-12-08T07:46:17Z
local.identifier.scopusID2-s2.0-33750943015
CollectionsANU Research Publications

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