3D beamforming based on deep learning for secure communication in 5G and beyond wireless networks

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...

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Main Authors: Yang, Helin, Lam, Kwok-Yan, Nie, Jiangtian, Zhao, Jun, Garg, Sahil, Xiao, Liang, Xiong, Zehui, Guizani, Mohsen
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/157422
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1574222022-07-22T07:17:43Z 3D beamforming based on deep learning for secure communication in 5G and beyond wireless networks Yang, Helin Lam, Kwok-Yan Nie, Jiangtian Zhao, Jun Garg, Sahil Xiao, Liang Xiong, Zehui Guizani, Mohsen School of Computer Science and Engineering 2021 IEEE Globecom Workshops (GC Wkshps) Nanyang Technopreneurship Center Strategic Centre for Research in Privacy-Preserving Technologies & Systems Engineering::Computer science and engineering 3D Beamforming Physical Layer Security 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. Info-communications Media Development Authority (IMDA) National Research Foundation (NRF) Submitted/Accepted version This research is supported by the National Research Foundation, Singapore under its Strategic Capability Research Centres Funding Initiative, Nanyang Technological University (NTU) Startup Grant, and SUTD SRG-ISTD-2021-165. 2022-05-12T02:13:39Z 2022-05-12T02:13:39Z 2022 Journal Article 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 9781665423908 https://hdl.handle.net/10356/157422 10.1109/GCWkshps52748.2021.9681960 2-s2.0-85126134387 en © 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. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
3D Beamforming
Physical Layer Security
spellingShingle Engineering::Computer science and engineering
3D Beamforming
Physical Layer Security
Yang, Helin
Lam, Kwok-Yan
Nie, Jiangtian
Zhao, Jun
Garg, Sahil
Xiao, Liang
Xiong, Zehui
Guizani, Mohsen
3D beamforming based on deep learning for secure communication in 5G and beyond wireless networks
description 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.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Yang, Helin
Lam, Kwok-Yan
Nie, Jiangtian
Zhao, Jun
Garg, Sahil
Xiao, Liang
Xiong, Zehui
Guizani, Mohsen
format Article
author Yang, Helin
Lam, Kwok-Yan
Nie, Jiangtian
Zhao, Jun
Garg, Sahil
Xiao, Liang
Xiong, Zehui
Guizani, Mohsen
author_sort Yang, Helin
title 3D beamforming based on deep learning for secure communication in 5G and beyond wireless networks
title_short 3D beamforming based on deep learning for secure communication in 5G and beyond wireless networks
title_full 3D beamforming based on deep learning for secure communication in 5G and beyond wireless networks
title_fullStr 3D beamforming based on deep learning for secure communication in 5G and beyond wireless networks
title_full_unstemmed 3D beamforming based on deep learning for secure communication in 5G and beyond wireless networks
title_sort 3d beamforming based on deep learning for secure communication in 5g and beyond wireless networks
publishDate 2022
url https://hdl.handle.net/10356/157422
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