Forcible search with mixed gibbs sampling massive MIMO detection

Kenji Yamazaki, Yukitoshi Sanada

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

Abstract

In this paper, mixed Gibbs sampling multiple- input multiple-output (MIMO) detection with forcible search is proposed. In conventional Gibbs sampling MIMO detection, the problem of stalling occurs under high signal-to-noise ratios (SNRs) which degrades the detection performance. Mixed GS (MGS) is one solution to this problem. In the MGS, random sampling is carried out with a constant probability without judging if a current search is at a local minimum. In the proposed scheme, combined with MGS, multiple candidate symbols are forcibly changed when the search is captured by a local minimum. The search restarts away from the local minimum which effectively enlarges the search area in a solution space. Numerical results obtained through computer simulation show that the proposed scheme achieves better performance in a large scale MIMO system with QPSK signals.

Original languageEnglish
Title of host publication2020 IEEE Region 10 Conference, TENCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages292-296
Number of pages5
ISBN (Electronic)9781728184555
DOIs
Publication statusPublished - 2020 Nov 16
Event2020 IEEE Region 10 Conference, TENCON 2020 - Virtual, Osaka, Japan
Duration: 2020 Nov 162020 Nov 19

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2020-November
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2020 IEEE Region 10 Conference, TENCON 2020
Country/TerritoryJapan
CityVirtual, Osaka
Period20/11/1620/11/19

Keywords

  • Component
  • Formatting
  • Insert
  • Style
  • Styling

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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