Forcible search with mixed gibbs sampling massive MIMO detection

Kenji Yamazaki, Yukitoshi Sanada

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


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.
Number of pages5
ISBN (Electronic)9781728184555
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
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2020 IEEE Region 10 Conference, TENCON 2020
CityVirtual, Osaka


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ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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