Joint Secure Transmission and Trajectory Optimization for Reconfigurable Intelligent Surface-aided Non-Terrestrial Networks

XU Kexin; LONG Keping; LU Yang; ZHANG Haijun

doi:10.11999/JEIT240981

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 >

XU Kexin, LONG Keping, LU Yang, ZHANG Haijun. Joint Secure Transmission and Trajectory Optimization for Reconfigurable Intelligent Surface-aided Non-Terrestrial Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240981

Citation:

XU Kexin, LONG Keping, LU Yang, ZHANG Haijun. Joint Secure Transmission and Trajectory Optimization for Reconfigurable Intelligent Surface-aided Non-Terrestrial Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240981

Citation:

XU Kexin, LONG Keping, LU Yang, ZHANG Haijun. Joint Secure Transmission and Trajectory Optimization for Reconfigurable Intelligent Surface-aided Non-Terrestrial Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240981

PDF( 1431 KB)

Joint Secure Transmission and Trajectory Optimization for Reconfigurable Intelligent Surface-aided Non-Terrestrial Networks

doi: 10.11999/JEIT240981

1.
The School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
China Electric Power Research Institute Co. Ltd. , Beijing 102209, China

Funds: The National Natural Science Foundation of China (U2441227, U22B2003), The Defense Industrial Technology Development Program (JCKY2022110C010), The Fundamental Research Funds for the Central Universities (FRF-TP-22-002C2), The National Key Laboratory of Wireless Communications Foundation (IFN20230201)

Received Date: 2024-11-01
Rev Recd Date: 2025-02-24

Available Online: 2025-02-25

Abstract

Abstract

Objective The proliferation of technologies such as the Internet of Things, smart cities, and next-generation mobile communications has made Non-Terrestrial Networks (NTNs) increasingly important for global communication. Future communication systems are expected to rely heavily on NTNs to provide seamless global coverage and efficient data transmission. However, current NTNs face challenges, including limited coverage and link quality in direct satellite-to-ground user connections, as well as eavesdropping threats. To address these challenges, a system integrating Reconfigurable Intelligent Surfaces (RIS) with a twin-layer Deep Reinforcement Learning (DRL) algorithm is proposed. This approach aims to satisfy the system’s requirements for high transmission rates and enhanced security, improving the signal strength for legitimate users while facilitating real-time updates and optimization of channel state information in NTNs. Methods First, a RIS-aided downlink NTNs system using an Unmanned Aerial Vehicle (UAV) as a relay is established. To balance the system’s transmission rate and security requirements, the weighted sum of the satellite-to-UAV transmission rate and the secure rate of the legitimate ground user is designed as the system utility, which serves as the optimization objective. A joint optimization method based on the Twin-Twin Delayed Deep Deterministic Policy Gradient (TTD3) algorithm is then proposed. This method jointly optimizes satellite and UAV beamforming, the RIS phase shift matrix, and UAV trajectory. The algorithm divides the optimization problem into two layers for solution. The first-layer DRL optimizes satellite and UAV beamforming, as well as the RIS phase shift matrix. The second-layer DRL optimizes the UAV's trajectory based on its position, user mobility, and channel state information. The twin DRL shares the same reward function, guiding the agents in each layer to adjust their actions and explore optimal strategies, ultimately enhancing the system's utility. Results and Discussions (1) Compared to the Deep Deterministic Policy Gradient (DDPG), the proposed TTD3 algorithm exhibits smaller dynamic fluctuations, demonstrating greater stability and robustness (Fig. 2). (2) The UAV trajectory and user secrecy rate performance under four different schemes and algorithms show that the proposed method balances service for legitimate users. The UAV trajectory is smoother compared to that based on DDPG, and the overall user secrecy rate is also higher. This confirms that the proposed method can adapt to dynamically changing NTNs environments while improving user secrecy rates (Fig. 3, Fig. 4). (3) As the number of RIS reflecting elements increases, the degrees of freedom and precision of beamforming improve. Therefore, the overall user secrecy rates of different algorithms increase, resulting in enhanced system performance (Fig. 5). Conclusions This paper investigates a RIS-assisted downlink secure transmission system for NTNs, addressing the presence of eavesdropping threats. To meet the requirements of high transmission rates and security across different scenarios, the optimization objective is formulated as the weighted sum of the transmission rate from the satellite to the UAV and the secrecy rate of legitimate ground users. A TTD3-based joint optimization method for satellite and UAV beamforming, RIS phase shift matrix, and UAV trajectory is proposed. By adopting a twin-layer DRL structure, the beamforming and trajectory optimization subproblems are decoupled to maximize system utility. Simulation results validate the effectiveness of the proposed algorithm. Additionally, comparisons across different algorithms, RIS element counts, and schemes in high-security-demand scenarios demonstrate that the TTD3 algorithm is well-suited for dynamically changing NTNs environments and can significantly enhance system transmission performance. Future research will explore integrating emerging technologies, such as federated learning and meta-learning, to achieve distributed, low-latency policy optimization, thereby facilitating network resource optimization and interference analysis in large-scale, multi-satellite, and multi-UAV complex scenarios.
- Reconfigurable Intelligent Surface (RIS),
- Non-Terrestrial Networks (NTNs),
- Deep Reinforcement Learning (DRL),
- Secure transmission

FullText(HTML)

References(16)

References

[1]	AZARI M M, SOLANKI S, CHATZINOTAS S, et al. Evolution of non-terrestrial networks from 5G to 6G: A survey[J]. IEEE Communications Surveys & Tutorials, 2022, 24(4): 2633–2672. doi: 10.1109/COMST.2022.3199901.
[2]	ZHOU Di, SHENG Min, LI Jiandong, et al. Aerospace integrated networks innovation for empowering 6G: A survey and future challenges[J]. IEEE Communications Surveys & Tutorials, 2023, 25(2): 975–1019. doi: 10.1109/COMST.2023.3245614.
[3]	JIANG Bin, YAN Yingchun, YOU Li, et al. Robust secure transmission for satellite communications[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(2): 1598–1612. doi: 10.1109/TAES.2022.3203027.
[4]	LI Yabo, ZHANG Haijun, LONG Keping, et al. Exploring sum rate maximization in UAV-based Multi-IRS networks: IRS association, UAV altitude, and phase shift design[J]. IEEE Transactions on Communications, 2022, 70(11): 7764–7774. doi: 10.1109/TCOMM.2022.3206884.
[5]	KHAN W U, LAGUNAS E, MAHMOOD A, et al. RIS-assisted energy-efficient LEO satellite communications with NOMA[J]. IEEE Transactions on Green Communications and Networking, 2024, 8(2): 780–790. doi: 10.1109/TGCN.2023.3344102.
[6]	ZHANG Haijun, HUANG Miaolin, ZHOU Huan, et al. Capacity maximization in RIS-UAV networks: A DDQN-based trajectory and phase shift optimization approach[J]. IEEE Transactions on Wireless Communications, 2023, 22(4): 2583–2591. doi: 10.1109/TWC.2022.3212830.
[7]	KHAN W U, LAGUNAS E, ALI Z, et al. Opportunities for physical layer security in UAV communication enhanced with intelligent reflective surfaces[J]. IEEE Wireless Communications, 2022, 29(6): 22–28. doi: 10.1109/MWC.001.2200125.
[8]	LI Jingyi, XU Sai, LIU Jiajia, et al. Reconfigurable intelligent surface enhanced secure aerial-ground communication[J]. IEEE Transactions on Communications, 2021, 69(9): 6185–6197. doi: 10.1109/TCOMM.2021.3086517.
[9]	GUO Xufeng, CHEN Yuanbin, and WANG Ying. Learning-based robust and secure transmission for reconfigurable intelligent surface aided millimeter wave UAV communications[J]. IEEE Wireless Communications Letters, 2021, 10(8): 1795–1799. doi: 10.1109/LWC.2021.3081464.
[10]	YANG Helin, LIU Shuai, XIAO Liang, et al. Learning-based reliable and secure transmission for UAV-RIS-assisted communication systems[J]. IEEE Transactions on Wireless Communications, 2024, 23(7): 6954–6967. doi: 10.1109/TWC.2023.3336535.
[11]	赵柏, 林敏, 肖圣杰, 等. 基于速率分割的可重构智能表面辅助星地融合网络鲁棒安全传输方案[J]. 通信学报, 2023, 44(12): 50–60. doi: 10.11959/j.issn.1000-436x.2023221. ZHAO Bai, LIN Min, XIAO Shengjie, et al. Rate splitting based robust secure transmission scheme in RIS-assisted satellite-terrestrial integrated network[J]. Journal on Communications, 2023, 44(12): 50–60. doi: 10.11959/j.issn.1000-436x.2023221.
[12]	ZHAO Bai, LIN Min, CHENG Ming, et al. Robust downlink transmission design in IRS-assisted cognitive satellite and terrestrial networks[J]. IEEE Journal on Selected Areas in Communications, 2023, 41(8): 2514–2529. doi: 10.1109/JSAC.2023.3288234.
[13]	NGO Q T, PHAN K T, MAHMOOD A, et al. Hybrid IRS-assisted secure satellite downlink communications: A fast deep reinforcement learning approach[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8(4): 2858–2869. doi: 10.1109/TETCI.2024.3378605.
[14]	LI Huifang, LI Jing, LIU Meng, et al. UAV-assisted secure communication for coordinated satellite-terrestrial networks[J]. IEEE Communications Letters, 2023, 27(7): 1709–1713. doi: 10.1109/LCOMM.2023.3267119.
[15]	ZHOU Gui, PAN Chunhua, REN Hong, et al. Stochastic learning-based robust beamforming design for RIS-aided millimeter-wave systems in the presence of random blockages[J]. IEEE Transactions on Vehicular Technology, 2021, 70(1): 1057–1061. doi: 10.1109/TVT.2021.3049257.
[16]	LIN Zhi, NIU Hehao, AN Kang, et al. Refracting RIS-aided hybrid satellite-terrestrial relay networks: Joint beamforming design and optimization[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(4): 3717–3724. doi: 10.1109/TAES.2022.3155711.