Abstract
Intelligent reflecting surface (IRS) has drawn great amount of attention from the researchers recently. It is worth investigating to maximize the spectral efficiency (SE) by jointly optimizing the beamforming at the access point (AP) and the phase shifts of the IRS. Although the traditional iterative algorithm can achieve high SE, it is not suitable for practical implementation due to its high computational complexity. Unsupervised learning could reduce the computational complexity, but as the number of IRS elements increases, the performance of SE becomes unsatisfactory. In this paper, we investigated a new deep learning method to maximize the SE in IRS assisted multiple-input single-output (MISO) communication system. Simulation results show that the performance of SE is better than that of the unsupervised learning method.
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Acknowledgements
This work is supported in part by National Natural Science Foundation of China (No. 61401118, and No. 61671184), Natural Science Foundation of Shandong Province (No. ZR2018PF001 and No. ZR2014FP016).
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Zhang, C., Zhu, X., Yang, H., Li, B. (2021). Deep Learning for Optimization of Intelligent Reflecting Surface Assisted MISO Systems. In: Liang, Q., Wang, W., Liu, X., Na, Z., Li, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2020. Lecture Notes in Electrical Engineering, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-15-8411-4_52
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DOI: https://doi.org/10.1007/978-981-15-8411-4_52
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