Low Complexity Metric Function for Gibbs Sampling MIMO Detection

Yutaro Kobayashi, Yukitoshi Sanada

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    In this paper, a metric function for Gibbs sampling multiple-input multiple-output (MIMO) detection is proposed. In conventional Gibbs sampling MIMO detection, an exponential function is used in the calculation of a metric for the selection of candidate symbols. However, the exponential function can be implemented by a look-up table and may require a large amount of memory. This paper proposes a metric function based on a simple fraction. The proposed metric substitutes the exponential function though it increases the number of multiplication operations. It is shown by numerical results obtained through computer simulation that the proposed metric function improves the performance under a high signal-to-noise ratio condition in a large scale MIMO system since its curve is close to that of the exponential function when an input metric distance approaches to zero.

    Original languageEnglish
    Title of host publication2018 IEEE 88th Vehicular Technology Conference, VTC-Fall 2018 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781538663585
    Publication statusPublished - 2019 Apr 12
    Event88th IEEE Vehicular Technology Conference, VTC-Fall 2018 - Chicago, United States
    Duration: 2018 Aug 272018 Aug 30

    Publication series

    NameIEEE Vehicular Technology Conference
    ISSN (Print)1550-2252


    Conference88th IEEE Vehicular Technology Conference, VTC-Fall 2018
    CountryUnited States

    ASJC Scopus subject areas

    • Computer Science Applications
    • Electrical and Electronic Engineering
    • Applied Mathematics

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