Optimal implicit channel estimation for finite state Markov communication channels

Zarko B. Krusevac*, Rodney A. Kennedy, Predrag B. Rapajic

*Corresponding author for this work

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

    6 Citations (Scopus)

    Abstract

    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 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).

    Original languageEnglish
    Title of host publicationProceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006
    Pages2657-2661
    Number of pages5
    DOIs
    Publication statusPublished - 2006
    Event2006 IEEE International Symposium on Information Theory, ISIT 2006 - Seattle, WA, United States
    Duration: 9 Jul 200614 Jul 2006

    Publication series

    NameIEEE International Symposium on Information Theory - Proceedings
    ISSN (Print)2157-8101

    Conference

    Conference2006 IEEE International Symposium on Information Theory, ISIT 2006
    Country/TerritoryUnited States
    CitySeattle, WA
    Period9/07/0614/07/06

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