Joint Weighted and Truncated Nuclear Norm Minimization for Matrix Completion-Assisted mmWave MIMO Channel Estimation

Yunyi Li, Jianxun Liu, Chaoyang Chen, Guan Gui, Tomoaki Ohtsuki, Hikmet Sari

研究成果: Conference contribution

抄録

Matrix completion-assisted channel estimation is considered one of promising techniques in millimeter wave (mmWave) massive multiple input multiple output (MIMO) system by exploiting the low-rank property of channel matrix in the angle domain. However, existing channel estimation approaches are hard to achieve high accuracy due to the inevitable bias solution caused by nuclear norm based minimization (NNM). To address this problem, this paper proposes a novel matrix completion-assisted mmWave massive MIMO channel estimation method. We employ an effective and flexible rank function named joint weighted and truncated nuclear norm as relaxation of nuclear norm, and then construct an novel matrix completion model for channel estimation problem. Moreover, a popular framework of alternating direction method of multipliers (ADMM) is derived for minimization of the resulting optimization problem. Simulation results are provided to verify the proposed method that can flexibly and effectively improve the channel estimation accuracy with reliable convergence.

本文言語English
ホスト出版物のタイトル2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665482431
DOI
出版ステータスPublished - 2022
イベント95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring - Helsinki, Finland
継続期間: 2022 6月 192022 6月 22

出版物シリーズ

名前IEEE Vehicular Technology Conference
2022-June
ISSN(印刷版)1550-2252

Conference

Conference95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring
国/地域Finland
CityHelsinki
Period22/6/1922/6/22

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
  • 応用数学

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