Iterative multiuser detection based on Monte Carlo probabilistic data association

Zhenning Shi*, Mark Reed

*Corresponding author for this work

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

    Abstract

    Multiple-Access Interference (MAI) has been considered as a major performance-limiting factor in the next-generation CDMA systems. Multiuser detection (MUD) methods have been proposed to mitigate the MAI from the co-channel users by incoporating the cross-correlation properties between users. Recently, two classes of emerging techniques, probabilistic data association (PDA) and Markov Chain Monte Carlo (MCMC) methods, have been applied to the multiuser detection. In this paper, we present a new method, named Monte Carlo PDA (MC-PDA), that incorporates the concepts of both to give a more reliable inference of the CDMA symbols by appropriately modelling and updating the MAI. The methodology is general and can be applied to other communication channels.

    Original languageEnglish
    Title of host publicationProceedings of the 2005 IEEE International Symposium on Information Theory, ISIT 05
    Pages332-336
    Number of pages5
    DOIs
    Publication statusPublished - 2005
    Event2005 IEEE International Symposium on Information Theory, ISIT 05 - Adelaide, Australia
    Duration: 4 Sept 20059 Sept 2005

    Publication series

    NameIEEE International Symposium on Information Theory - Proceedings
    Volume2005
    ISSN (Print)2157-8099

    Conference

    Conference2005 IEEE International Symposium on Information Theory, ISIT 05
    Country/TerritoryAustralia
    CityAdelaide
    Period4/09/059/09/05

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