Fast Beamforming Design Method for IRS-Aided mmWave MISO Systems

Zhengran He, Hao Huang, Jie Yang, Guan Gui, Tomoaki Ohtsuki, Bamidele Adebisi, Haris Gacanin

研究成果: Conference contribution


Intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) multiple-input single-output (MISO) is considered one of the promising techniques in next-generation wireless communication. However, existing beamforming methods for IRS-aided mm Wave MISO systems require high computational power, so it cannot be widely used. In this paper, we combine an unsupervised learning-based fast beamforming method with IRS-aided MISO systems, to significantly reduce the computational complexity of this system. Specifically, a new beamforming design method is proposed by adopting the feature fusion means in unsupervised learning. By designing a specific loss function, the beamforming can be obtained to make the spectrum more efficient, and the complexity is lower than that of the existing algorithms. Simulation results show that the proposed beamforming method can effectively reduce the computational complexity while obtaining relatively good performance results.

ホスト出版物のタイトル2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
出版ステータスPublished - 2021
イベント94th IEEE Vehicular Technology Conference, VTC 2021-Fall - Virtual, Online, United States
継続期間: 2021 9月 272021 9月 30


名前IEEE Vehicular Technology Conference


Conference94th IEEE Vehicular Technology Conference, VTC 2021-Fall
国/地域United States
CityVirtual, Online

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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