Show simple item record

AuthorYang, Helin
AuthorLam, Kwok Yan
AuthorNie, Jiangtian
AuthorZhao, Jun
AuthorGarg, Sahil
AuthorXiao, Liang
AuthorXiong, Zehui
AuthorGuizani, Mohsen
Available date2022-11-10T08:19:22Z
Publication Date2021-01-01
Publication Name2021 IEEE Globecom Workshops, GC Wkshps 2021 - Proceedings
Identifierhttp://dx.doi.org/10.1109/GCWkshps52748.2021.9681960
CitationYang, H., Lam, K. Y., Nie, J., Zhao, J., Garg, S., Xiao, L., ... & Guizani, M. (2021, December). 3D Beamforming Based on Deep Learning for Secure Communication in 5G and Beyond Wireless Networks. In 2021 IEEE Globecom Workshops (GC Wkshps) (pp. 1-6). IEEE.‏
ISBN9781665423908
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85126134387&origin=inward
URIhttp://hdl.handle.net/10576/36071
AbstractThree-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.
SponsorThis 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.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subject3D beamforming
deep learning
physical layer security
secrecy rate maximization
wireless security
Title3D Beamforming Based on Deep Learning for Secure Communication in 5G and beyond Wireless Networks
TypeConference Paper
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record