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Beamforming Optimization for Intelligent Reflecting Surface Assisted MISO: A Deep Transfer Learning Approach.
- Source :
- IEEE Transactions on Vehicular Technology; Apr2021, Vol. 70 Issue 4, p3902-3907, 6p
- Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- MISO
BEAMFORMING
WIRELESS communications
DEEP learning
COMPUTATIONAL complexity
Subjects
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 70
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 150190401
- Full Text :
- https://doi.org/10.1109/TVT.2021.3062870