A new approach for sparse Bayesian channel estimation in SCMA uplink systems

Kirill Struminsky, Stanislav Kruglik, Dmitry Vetrov, Ivan Oseledets

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

    7 Citations (Scopus)

    Abstract

    The rapid growth of traffic and number of simultaneously available devices leads to the new challenges in constructing fifth generation wireless networks (5G). To handle with them various schemes of non-orthogonal multiple access (NOMA) were proposed. One of these schemes is Sparse Code Multiple Access (SCMA), which is shown to achieve better link level performance. In order to support SCMA signal decoding channel estimation is needed and sparse Bayesian learning framework may be used to reduce the requirement of pilot overhead. In this paper we propose a modification of sparse Bayesian learning based channel estimation algorithm that is shown to achieve better accuracy of user detection and faster convergence in numerical simulations.

    Original languageEnglish
    Title of host publication2016 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781509028603
    DOIs
    Publication statusPublished - 21 Nov 2016
    Event8th International Conference on Wireless Communications and Signal Processing, WCSP 2016 - Yangzhou, China
    Duration: 13 Oct 201615 Oct 2016

    Publication series

    Name2016 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016

    Conference

    Conference8th International Conference on Wireless Communications and Signal Processing, WCSP 2016
    Country/TerritoryChina
    CityYangzhou
    Period13/10/1615/10/16

    Keywords

    • 5G
    • active user detection
    • channel estimation
    • SCMA
    • sparse Bayesian learning

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