Wang, D., Li, J., Lv, Q., He, Y., Li, L., Hua, Q., Alfarraj, O. and Zhang, J., 2024. Integrating Reconfigurable Intelligent Surface and UAV for Enhanced Secure Transmissions in IoT-Enabled RSMA Networks. IEEE Internet of Things Journal. (In Press)
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DOI: 10.1109/JIOT.2024.3523500
Abstract
Unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) exhibits great application potential with its wide coverage, flexible network topology, and diversified services. However, ensuring communication security and efficient spectrum resource utilization in multi-user access scenarios is challenging, given the open nature of UAV channels and the proliferation of communication devices in IoT. To address the above challenges, this paper proposes a novel reconfigurable intelligent surface (RIS)-aided UAV collaborative communication framework, where RIS-equipped UAV flexibly serves multiple users. In this work, a rate splitting multiple access (RSMA)-based secure transmission scheme is proposed, where the split public information serves both as useful signals and noise to disrupt eavesdropping. For the proposed scheme, a sum secrecy rate maximization problem is formulated and solved by optimally deploying the UAV's location, designing the RIS's phase shift, and power allocation. For this non-convex problem with a couple of variables, we decompose it and form three separate sub-issues. Specifically, leveraging the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques, we first exploit an iterative algorithm for optimizing beamforming vectors and phase-shift matrix of RIS, and the optimal position of the UAV is obtained according to the deep deterministic policy gradient (DDPG). Then, we design an alternating optimization (AO) framework for joint solving. Finally, simulation results validate the efficacy of the proposed scheme in enhancing security, e.g., relative to the non-orthogonal multiple access (NOMA) scheme and benchmark scheme, the secrecy rate of the proposed scheme increased by 29.7% and 71.9% respectively.
Item Type: | Article |
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ISSN: | 2327-4662 |
Group: | Faculty of Science & Technology |
ID Code: | 40711 |
Deposited By: | Symplectic RT2 |
Deposited On: | 22 Jan 2025 10:59 |
Last Modified: | 22 Jan 2025 10:59 |
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