Leveraging Machine Learning for Millimeter Wave Beamforming in Beyond 5G Networks

Basem M. Elhalawany, Sherief Hashima, Kohei Hatano, Kaishun Wu, Ehab Mahmoud Mohamed

研究成果: ジャーナルへの寄稿学術誌査読

8 被引用数 (Scopus)

抄録

Millimeter wave (mmWave) communication has attracted considerable attention as a key technology for the next-generation wireless communications thanks to its exceptional advantages. MmWave leads the way to achieve a high transmission quality with directed narrow beams from source to multiple destinations by adopting different antenna beamforming (BF) techniques, which have a pivotal role in establishing and maintaining robust links. However, realizing such BF gains in practice requires overcoming several challenges, such as severe signal deterioration, hardware constraints, and design complexity. The elevated complexity of configuring mmWave BF vectors encourages researchers to leverage relevant machine learning (ML) techniques for better BF configurations deployment in 5G and beyond. In this article, we summarize mmWave BF strategies employed for future wireless networks. Then, we provide a comprehensive overview of ML techniques plus its applications and promising contributions toward efficient mmWave BF deployment. Furthermore, we discuss mmWave BF's future research directions and challenges. Finally, we discuss a single and concurrent mmWave BF case study by applying multiarmed bandit to confirm the superiority of ML-based methods over conventional ones.

本文言語英語
ページ(範囲)1739-1750
ページ数12
ジャーナルIEEE Systems Journal
16
2
DOI
出版ステータス出版済み - 6月 1 2022

!!!All Science Journal Classification (ASJC) codes

  • 制御およびシステム工学
  • 情報システム
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学

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