Forcible search with mixed gibbs sampling massive MIMO detection

Kenji Yamazaki, Yukitoshi Sanada

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2020 IEEE Region 10 Conference, TENCON 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ292-296
ページ数5
ISBN(電子版)9781728184555
DOI
出版ステータスPublished - 2020 11 16
イベント2020 IEEE Region 10 Conference, TENCON 2020 - Virtual, Osaka, Japan
継続期間: 2020 11 162020 11 19

出版物シリーズ

名前IEEE Region 10 Annual International Conference, Proceedings/TENCON
2020-November
ISSN(印刷版)2159-3442
ISSN(電子版)2159-3450

Conference

Conference2020 IEEE Region 10 Conference, TENCON 2020
国/地域Japan
CityVirtual, Osaka
Period20/11/1620/11/19

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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