TY - GEN
T1 - Fast Beamforming Design Method for IRS-Aided mmWave MISO Systems
AU - He, Zhengran
AU - Huang, Hao
AU - Yang, Jie
AU - Gui, Guan
AU - Ohtsuki, Tomoaki
AU - Adebisi, Bamidele
AU - Gacanin, Haris
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Millimeter-wave
KW - beamforming
KW - intelligent reconfigurable surface
KW - unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85122975328&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85122975328&partnerID=8YFLogxK
U2 - 10.1109/VTC2021-Fall52928.2021.9625497
DO - 10.1109/VTC2021-Fall52928.2021.9625497
M3 - Conference contribution
AN - SCOPUS:85122975328
T3 - IEEE Vehicular Technology Conference
BT - 2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 94th IEEE Vehicular Technology Conference, VTC 2021-Fall
Y2 - 27 September 2021 through 30 September 2021
ER -