Deep Learning Based Resource Allocation Method to Control System Capacity and Fairness for MU-MIMO THP

Yukiko Shimbo, Hirofumi Suganuma, Hiromichi Tomeba, Takashi Onodera, Fumiaki Maehara

研究成果: Conference contribution

抄録

This paper proposes a deep-learning-based resource allocation method to adaptively control system capacity and fairness for multi-user multiple-input and multiple-output (MU-MIMO). In the proposed method, Tomlinson-Harashima precoding (THP) is used to enhance the transmission rate. Additionally, channel resources are appropriately allocated based on user scheduling techniques, i.e., semiorthogonal user selection (SUS) for throughput maximization and proportional fairness (PF) for fairness among users. The primary feature of the proposed method is that it appropriately allocates channel resources by utilizing the user position information and target fairness index (FI) through deep learning. This makes it possible to meet various service requirements. Numerical simulations are used to demonstrate the effectiveness of the proposed method in terms of system capacity and fairness under different MIMO configurations and user distributions.

本文言語English
ホスト出版物のタイトル2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728189642
DOI
出版ステータスPublished - 2021 4
イベント93rd IEEE Vehicular Technology Conference, VTC 2021-Spring - Virtual, Online
継続期間: 2021 4 252021 4 28

出版物シリーズ

名前IEEE Vehicular Technology Conference
2021-April
ISSN(印刷版)1550-2252

Conference

Conference93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
CityVirtual, Online
Period21/4/2521/4/28

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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