Deep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization

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dc.contributor.authorLi, Jiawei
dc.contributor.authorWang, Dawei
dc.contributor.authorZhang, Jiankang
dc.contributor.authorAlfarraj, Osama
dc.contributor.authorHe, Yixin
dc.contributor.authorAl-Rubaye, Saba
dc.contributor.authorYu, Keping
dc.contributor.authorMumtaz, Shahid
dc.date.accessioned2025-04-14T15:04:49Z
dc.date.available2025-04-14T15:04:49Z
dc.date.freetoread2025-04-14
dc.date.issued2024-12-08
dc.date.pubOnline2025-03-11
dc.description.abstractThis paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.
dc.description.conferencenameGLOBECOM 2024 - 2024 IEEE Global Communications Conference
dc.description.sponsorshipKing Saud University; JCYJ20190806160218174
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grants 62271399 and 62206221, in part by National Key Research and Development Program of China under Grant 2020YFB1807003, in part by Foundation of the Science, Technology, and Innovation Commission of Shenzhen Municipality under Grant JCYJ20190806160218174, in part by Zhejiang Provincial Natural Science Foundation of China under Grant LQ24F010003, in part by the Distinguished Scientist Fellowship Program (DSFP) at King Saud University, Riyadh, Saudi Arabia, and in part by the Bournemouth University Qualiy research funding: Flying ad-hoc networking and its applications.
dc.format.extentpp. 1617-1622
dc.identifier.citationLi J, Wang D, Zhang J, et al., (2024) Deep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization. In: GLOBECOM 2024 - 2024 IEEE Global Communications Conference, 8-12 December 2024, Cape Town, South Africa, pp. 1617-1622
dc.identifier.eissn2576-6813
dc.identifier.elementsID567369
dc.identifier.issn2334-0983
dc.identifier.urihttps://doi.org/10.1109/globecom52923.2024.10901660
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23765
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10901660
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4613 Theory Of Computation
dc.subject46 Information and Computing Sciences
dc.subject40 Engineering
dc.titleDeep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization
dc.typeConference paper
dcterms.dateAccepted2024-07-30
dcterms.temporal.endDate12 Dec 2024
dcterms.temporal.startDate8 Dec 2024

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