Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157422
Title: 3D beamforming based on deep learning for secure communication in 5G and beyond wireless networks
Authors: Yang, Helin
Lam, Kwok-Yan
Nie, Jiangtian
Zhao, Jun
Garg, Sahil
Xiao, Liang
Xiong, Zehui
Guizani, Mohsen
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Yang, H., Lam, K., Nie, J., Zhao, J., Garg, S., Xiao, L., Xiong, Z. & Guizani, M. (2022). 3D beamforming based on deep learning for secure communication in 5G and beyond wireless networks. 2021 IEEE Globecom Workshops (GC Wkshps). https://dx.doi.org/10.1109/GCWkshps52748.2021.9681960
Abstract: Three-dimensional (3D) beamforming is a potential technique to enhance communication security of new generation networks such as 5G and beyond. However, it is difficult to achieve optimal beamforming due to the challenges of nonconvex optimization problem and imperfect channel state information (CSI). To tackle this problem, this paper proposes a novel deep learning-based 3D beamforming scheme, where a deep neural network (DNN) is trained to optimize the beamforming design for wireless signals in order to guard against eavesdropper under the imperfect CSI. With our approach, the system is capable of training the DNN model offline, and the trained model can then be adopted to instantaneously select the 3D secure beamforming matrix for achieving the maximum secrecy rate of the system, which is measured by the signal received by eavesdroppers outside the path of the beam. Simulation results demonstrate that the proposed solution outperforms the classical deep learning algorithm and 2D beamforming solution in terms of the secrecy rate and robust performance.
URI: https://hdl.handle.net/10356/157422
ISBN: 9781665423908
DOI: 10.1109/GCWkshps52748.2021.9681960
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/GCWkshps52748.2021.9681960.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:NTC Conference Papers
SCSE Conference Papers

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