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Optimal implicit channel estimation for 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. A proper quantification of source redundancy information, implicitly used for channel estimation, is performed. This enables an optimal training rate to be determined as a tradeoff between input signal entropy rate reduction (source redundancy) and channel process entropy rate reduction (channel...[Show more]

dc.contributor.authorKrusevac, Zarko
dc.contributor.authorKennedy, Rodney
dc.contributor.authorRapajic, Predrag
dc.coverage.spatialSeattle USA
dc.date.accessioned2015-12-08T22:08:44Z
dc.date.available2015-12-08T22:08:44Z
dc.date.createdJuly 9-14 2006
dc.identifier.isbn1424405041
dc.identifier.urihttp://hdl.handle.net/1885/28716
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. A proper quantification of source redundancy information, implicitly used for channel estimation, is performed. This enables an optimal training rate to be determined as a tradeoff between input signal entropy rate reduction (source redundancy) and channel process entropy rate reduction (channel estimation). The maximal mutual information rate, assuming the optimal implicit training and the presence of channel noise, is shown to be strictly below the ergodic channel information capacity. It is also shown that this capacity penalty, caused by noisy time-varying channel process estimation, vanishes only if the channel process is known or memoryless (channel estimation cannot improve system performance).
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesIEEE International Symposium on Information Theory (ISIT 2006)
dc.sourceIEEE International Symposium on Information Theory
dc.subjectKeywords: Feedback implicit estimators; Markov communication channels; Communication channels (information theory); Finite automata; Markov processes; Optimal systems; Channel estimation
dc.titleOptimal implicit channel estimation for finite state Markov communication channels
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2006
local.identifier.absfor090609 - Signal Processing
local.identifier.absfor100510 - Wireless Communications
local.identifier.ariespublicationu3357961xPUB60
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.startpage2657
local.bibliographicCitation.lastpage2661
local.identifier.doi10.1109/ISIT.2006.262135
dc.date.updated2015-12-08T07:19:10Z
local.identifier.scopusID2-s2.0-39049145810
CollectionsANU Research Publications

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