Articles
Vol. 1 No. 4 (2026)
RIS-Assisted Secure Transmission for Unmanned Aerial Vehicle and Satellite Links Under Hardware Impairments
The Department of Automatic Control and Systems Engineering (ACSE), Faculty of Engineering, The University of Sheffield, Sheffield S10 2TN, UK
-
Submitted
-
August 7, 2026
-
Published
-
August 7, 2026
Abstract
The integration of satellite networks with unmanned aerial vehicles has emerged as a cornerstone for next-generation communication systems, providing ubiquitous connectivity and expansive coverage. However, the inherent broadcast nature of wireless channels in these open space environments renders them highly susceptible to malicious eavesdropping. Reconfigurable intelligent surfaces have recently been proposed as a revolutionary technology to enhance physical layer security by dynamically altering the propagation environment. Despite their potential, the practical implementation of reconfigurable intelligent surfaces and transceiver modules is inevitably constrained by hardware impairments, such as phase noise, in-phase and quadrature imbalance, and amplifier nonlinearities. This paper provides a comprehensive analysis of secure transmission in a reconfigurable intelligent surface-assisted satellite and unmanned aerial vehicle network subjected to realistic hardware impairments. We formulate a robust optimization problem aimed at maximizing the secrecy rate of the system by jointly designing the active beamforming at the satellite and the passive phase shift matrix at the reconfigurable intelligent surface, while explicitly accounting for the distortion noise introduced by hardware imperfections. Through an alternating optimization framework based on successive convex approximation, we navigate the non-convex nature of the objective function. Extensive theoretical analysis and system-level evaluations reveal that ignoring hardware impairments leads to significant performance degradation, highlighting the necessity of hardware-aware robust designs in practical space-air-ground integrated networks.
References
- 1. Li, X., Hu, J., & Liu, X. (2020). Harmonic Distortion Optimization for Sigma-Delta Modulators Interface Circuit of TMR Sensors.Sensors,20(4), 1041.
- 2. Yang, Z., Ji, W., Guo, Q., & Wang, Z. (2023, October). Javp: Joint-aware video processing with edge-cloud collaboration for dnn inference. In Proceedings of the 31st ACM International Conference on Multimedia (pp. 9152-9160).
- 3. Yang, Z., Ji, W., & Wang, Z. (2024). Adaptive joint configuration optimization for collaborative inference in edge-cloud systems. Science China Information Sciences, 67(4), 149103.
- 4. Tian, Y., & Yang, Z. (2026, May). SAEC: scene-aware enhanced edge-cloud collaborative industrial vision inspection with multimodal LLM. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 19747-19751). IEEE.
- 5. Xia, L., Yang, M., Wang, J., Yan, Z., Ren, Y., Yu, G., & Lei, K. (2025, September). Mamba4Net: Distilled Hybrid Mamba Large Language Models For Networking. In2025 IEEE 33rd International Conference on Network Protocols (ICNP)(pp. 1-10). IEEE.
- 6. Yang, Z., Liang, B., & Ji, W. (2021). An intelligent end–edge–cloud architecture for visual IoT-assisted healthcare systems. IEEE internet of things journal, 8(23), 16779-16786.
- 7. Luo, H., Li, Q., Cheng, H., Li, W., Sun, W., Zhao, W., & Liu, Z. (2025). A²Tformer: Addressing temporal bias and non-stationarity in transformer-based IoT time series classification. IEEE Internet of Things Journal, 12(20), 42198–42213.
- 8. Zhang, W., Li, M., & Chen, X. (2026). Distributed Edge-Fog-Cloud Architecture for Intelligent Fault Diagnosis of Permanent Magnet Synchronous Motors Using Temporal Fusion Transformers and Federated Learning. Fundamental Scientific Reports in Multidisciplinary Areas, 2 (02), 121-131.
- 9. Hu, Y., Yang, Z., Zhao, C., & Ji, W. (2026, May). Adaptive guidance semantically enhanced via multimodal llm for edge-cloud object detection. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 12962-12966). IEEE.
- 10. Li, X., Wang, P., Li, G., Ni, L., & Zhang, Y. (2023). Design of interface circuits and lightweight PUF for TMR sensors. IEEE Sensors Journal, 23(11), 11754-11761.
- 11. Zhao, Q., & Huang, Y. (2026). DAREA: A Dynamic AI-Driven Architecture for Real Estate Asset Tokenization on Blockchain.Fundamental Scientific Reports in Multidisciplinary Areas,2(02), 182-192.
- 12. Xu, C., Wang, C., & Li, Z. (2026). H2-LBM: A Hierarchical Hybrid Deep Reinforcement Learning Framework for L7 Load Balancing and Global Traffic Scheduling in Multi-Cloud LLM Serving. Fundamental Scientific Reports in Multidisciplinary Areas, 2 (02), 109-120.
- 13. Corbett, J. C., Dean, J., Epstein, M., Fikes, A., Frost, C., Furman, J. J., et al. (2013). Spanner: Google’s globally-distributed database. ACM Transactions on Computer Systems, 31(3), Article 8.
- 14. Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016). Borg, Omega, and Kubernetes. Communications of the ACM, 59(5), 50–57.
- 15. Yao, Y., Sun, J., Miao, P., Chen, G., & Tafazolli, R. (2026). Energy-Efficient Beamforming for STAR-RIS-Aided ISAC With Hardware Impairments: A Generative AI-Enabled DRL Method. IEEE Transactions on Wireless Communications.
- 16. Yang, W., Qin, Y., Jiang, Z., & Chu, X. (2021, June). Traffic Management for Distributed Machine Learning in RDMA-enabled Data Center Networks. In ICC 2021-IEEE International Conference on Communications (pp. 1-6). IEEE.
- 17. Li, Y., Zhou, Y., Zhang, Y., Wang, P., Zhang, Y., Li, X., & Zhang, X. (2025). Nonvolatile Optical Switch With a Programmable pn Heterojunction.IEEE Transactions on Electron Devices,72(8), 4030-4035.
- 18. Yang, W., Qin, Y., & Yang, Z. (2022). A reinforcement learning based data storage and traffic management in information-centric data center networks. Mobile Networks and Applications, 27(1), 266-275.
- 19. Long, J., Xu, Z., Jiang, T., Yao, W., Jia, S., Ma, C., & Chen, X. (2025, April). Robust SAM: on the adversarial robustness of vision foundation models. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 6, pp. 5775-5783).
- 20. Yao, Y., Lu, Z., Chen, G., Huang, C., Feng, C., & Quek, T. Q. (2026). Interference Management in ISAC-SAGINs Based on Transformer-Enabled Mean-Field Reinforcement Learning Method. IEEE Transactions on Wireless Communications.
- 21. Wan, H., Zhang, Y., Wang, J., Wu, D., Li, M., Chen, X., ... & Ji, X. (2025). Toward universal embodied planning in scalable heterogeneous field robots collaboration and control. Journal of Field Robotics, 42(5), 2318-2336.
- 22. Ren, S., Li, X., Shao, Z., Gong, M., & Ren, M. (2025). A Hybrid Structure Noise Shaping SAR ADC.Journal of Circuits, Systems and Computers,34(05), 2550135.
- 23. Xiao, W., Tan, Q., Chen, M., Yao, Y., & Shu, F. (2026). Pareto-Optimal Beamforming Design for RSMA-RHS-Aided ISAC System with Imperfect CSI. IEEE Transactions on Vehicular Technology.
- 24. Yang, Y., Ma, X., Li, C., Zheng, Z., Zhang, Q., Huang, G., ... & Zhao, Q. (2021). Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 34, 10299-10312.
- 25. Wang, S., Zhang, X., Wang, P., & Li, X. (2026). Design of Magnetic Sensor Array-Based PUFs for IoT Security. IEEE Transactions on Instrumentation and Measurement, 75, 1-9.