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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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