Deep Learning Based Secure Transmissions for the UAV-RIS Assisted Networks: Trajectory and Phase Shift Optimization
Authors: Li, J., Wang, D., Zhang, J., Alfarraj, O., He, Y., Al-Rubaye, S., Yu, K. and Mumtaz, S.
Journal: Proceedings IEEE Global Communications Conference Globecom
Pages: 1617-1622
eISSN: 2576-6813
ISSN: 2334-0983
DOI: 10.1109/GLOBECOM52923.2024.10901660
Abstract:This 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.
https://eprints.bournemouth.ac.uk/40300/
Source: Scopus
Deep Learning Based Secure Transmissions for the UAV-RIS Assisted Networks: Trajectory and Phase Shift Optimization
Authors: Li, J., Wang, D., Zhang, J., Alfarraj, O., He, Y., Al-Rubaye, S., Yu, K. and Mumtaz, S.
Journal: GLOBECOM 2024-2024 IEEE GLOBAL COMMUNICATIONS CONFERENCE
Pages: 1617-1622
ISBN: 979-8-3503-5126-2
ISSN: 1930-529X
DOI: 10.1109/GLOBECOM52923.2024.10901660
https://eprints.bournemouth.ac.uk/40300/
Source: Web of Science (Lite)
Deep Learning Based Secure Transmissions for the UAV-RIS Assisted Networks: Trajectory and Phase Shift Optimization
Authors: Li, J., Wang, D., Zhang, J., Alfarraj, A., He, Y., Al-Rubaye, S., Yu, K. and Mumtaz, M.
Conference: IEEE Global Communications Conference (GLOBECOM)
Dates: 8-12 December 2024
https://eprints.bournemouth.ac.uk/40300/
Source: Manual
Deep learning based secure transmissions for the UAV-RIS assisted networks: Trajectory and phase shift optimization
Authors: Li, J., Wang, D., Zhang, J., Alfarraj, O., He, Y., Al-Rubaye, S., Yu, K. and Mumtaz, S.
Pages: 1617-1622
Publisher: IEEE
Place of Publication: New York, NY
ISBN: 9798350351255
Abstract:This 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.
https://eprints.bournemouth.ac.uk/40300/
https://globecom2024.ieee-globecom.org/
Source: BURO EPrints