Institute for Communication Technologies and Embedded Systems

Fast Beamforming Design Method for IRS-Aided MISO Systems

Authors:
He, Z. ,  Huang, H. ,  Yang, J. ,  Gui, G. ,  Ohtsuki, T. ,  Abedisi, B. ,  Gacanin, H.
Book Title:
IEEEVTC 2021-Fall, 27 Sept. - 30 Sept. 2021, Virtual conference
Pages:
p.p. 1-5
Date:
Sep. 2021
DOI:
10.1109/VTC2021-Fall52928.2021.9625497
hsb:
RWTH-2021-11883
Language:
English
Abstract:
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.
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