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• W3134205660 abstract "This article studies the beamforming optimization for intelligent reflecting surface (IRS) assisted multiple-input single-output (MISO) wireless communication system. We establish a deep transfer learning (DTL)-based framework to learn how to optimize the phase shifts at the IRS side. Based on it, we also design a loss function to implement unsupervised training without a large number of labeled data samples. Finally, we extend the optimization problem to discrete phase shift constraint to solve the hardware limitation. The simulation verifies that the proposed DTL-based approach can achieve similar performance compared with upper bound while substantially reducing the computational complexity." @default.
• W3134205660 created "2021-03-15" @default.
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• W3134205660 date "2021-04-01" @default.
• W3134205660 modified "2023-09-30" @default.
• W3134205660 title "Beamforming Optimization for Intelligent Reflecting Surface Assisted MISO: A Deep Transfer Learning Approach" @default.
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• W3134205660 doi "https://doi.org/10.1109/tvt.2021.3062870" @default.
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