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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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 |
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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 |
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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. |
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School of Computer Science and Engineering |
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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 |
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2022 |
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https://hdl.handle.net/10356/157422 |
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