Joint UAV Trajectory, IRS Beamforming, and Power Allocation Optimization for Secure Communication in IRS-Assisted Cognitive Networks

Xiaoyan Liu

Abstract


Based on the challenges faced by unmanned aerial vehicle communication in urban environments, such as signal blockage caused by building obstruction, limited spectrum resources, and dynamic security threats, this article aims to improve the safety communication performance of unmanned aerial vehicles through intelligent reflective surface (IRS) technology, and solve the problems of signal obstruction and cognitive radio interference. Moreover, this paper proposes an innovative multi-parameter collaborative optimization method, constructs a three-dimensional channel model including Rayleigh/Rice fading, designs an IRS-trajectory-power three-dimensional coordination algorithm based on alternating optimization, and solves the IRS phase shift matrix through semi-steady relaxation (SDR), optimizes the UAV trajectory through continuous convex approximation (SCA), and allocates power through Lagrangian dual decomposition to achieve iterative updates of the three modules. Numerical simulation verification: 1) The safe capacity reaches 1.85 ± 0.12 bit/s/Hz, which is 30.3% higher than the optimal control group; 2) The interference suppression rate is 94.3 ± 2.1%, and the NFZ (No fly zones) avoidance success rate is 100%; 3) The energy efficiency is 1.58 ± 0.11 bit/Joule, which is 30.6% higher than the SOTA model. The conclusion shows that the three-dimensional collaborative mechanism of IRS trajectory power can break through the bottleneck of traditional optimization, and its core contribution lies in: 1) solving the shortcomings of existing research in dynamic security response, multi device interference suppression, and energy consumption balance; 2) providing robust solutions for high mobility urban scenarios.


Full Text:

PDF

References


Pang, X., Sheng, M., Zhao, N., Tang, J., Niyato, D., & Wong, K. K. (2021). When UAV meets IRS: Expanding air-ground networks via passive reflection. IEEE Wireless Communications, 28(5), 164-170. https://doi.org/10.1109/MWC.010.2000528

Pang, X., Zhao, N., Tang, J., Wu, C., Niyato, D., & Wong, K. K. (2021). IRS-assisted secure UAV transmission via joint trajectory and beamforming design. IEEE Transactions on Communications, 70(2), 1140-1152. https://doi.org/10.1109/tcomm。36860.88868688666

You, C., Kang, Z., Zeng, Y., & Zhang, R. (2021). Enabling smart reflection in integrated air-ground wireless network: IRS meets UAV. IEEE Wireless Communications, 28(6), 138-144. https://doi.org/10.48550/arXiv.2103.07151

Yu, J., Liu, X., Gao, Y., Zhang, C., & Zhang, W. (2022). Deep learning for channel tracking in IRS-assisted UAV communication systems. IEEE Transactions on Wireless Communications, 21(9), 7711-7722. https://doi.org/10.1109/TWC.2022.3160517

Malik, S., Saxena, P., & Chung, Y. H. (2022). Performance analysis of a UAV-based IRS-assisted hybrid RF/FSO link with pointing and phase shift errors. Journal of Optical Communications and Networking, 14(4), 303-315. https://doi.org/10.1364/JOCN.451410

Li, Y., Zhang, H., Long, K., & Nallanathan, A. (2022). Exploring sum rate maximization in UAV-based multi-IRS networks: IRS association, UAV altitude, and phase shift design. IEEE Transactions on Communications, 70(11), 7764-7774. https://doi.org/10.1109/TCOMM.2022.3206884

Su, Y., Pang, X., Chen, S., Jiang, X., Zhao, N., & Yu, F. R. (2022). Spectrum and energy efficiency optimization in IRS-assisted UAV networks. IEEE Transactions on Communications, 70(10), 6489-6502. https://doi.org/10.1109/TCOMM.2022.3201122

Wang, W., Tian, H., & Ni, W. (2021). Secrecy performance analysis of IRS-aided UAV relay system. IEEE Wireless Communications Letters, 10(12), 2693-2697. https://doi.org/10.1109/LWC.2021.3112752

Al-Jarrah, M., Al-Dweik, A., Alsusa, E., Iraqi, Y., & Alouini, M. S. (2021). On the performance of IRS-assisted multi-layer UAV communications with imperfect phase compensation. IEEE Transactions on Communications, 69(12), 8551-8568. https://doi.org/10.1109/tcomm.2021.3113008

Solanki, S., Park, J., & Lee, I. (2022). On the performance of IRS-aided UAV networks with NOMA. IEEE Transactions on Vehicular Technology, 71(8), 9038-9043. https://doi.org/10.1109/TVT.2022.3171271

Cai, Y., Wei, Z., Hu, S., Liu, C., Ng, D. W. K., & Yuan, J. (2022). Resource allocation and 3D trajectory design for power-efficient IRS-assisted UAV-NOMA communications. IEEE Transactions on Wireless Communications, 21(12), 10315-10334. https://doi.org/10.1109/TWC.2022.3183300

Liu, Z., Zhao, S., Wu, Q., Yang, Y., & Guan, X. (2021). Joint trajectory design and resource allocation for IRS-assisted UAV communications with wireless energy harvesting. IEEE Communications Letters, 26(2), 404-408. https://doi.org/10.1109/LCOMM.2021.3128545

Asim, M., ELAffendi, M., & Abd El-Latif, A. A. (2022). Multi-IRS and multi-UAV-assisted MEC system for 5G/6G networks: Efficient joint trajectory optimization and passive beamforming framework. IEEE Transactions on Intelligent Transportation Systems, 24(4), 4553-4564. https://doi.org/10.1109/TITS.2022.3178896

Zhang, X., Wang, J., & Poor, H. V. (2022). Joint optimization of IRS and UAV-trajectory: For supporting statistical delay and error-rate bounded QoS over mURLLC-driven 6G mobile wireless networks using FBC. IEEE Vehicular Technology Magazine, 17(2), 55-63. https://doi.org/10.1109/MVT。59660.88868886666

Saxena, P., & Chung, Y. H. (2023). Analysis of jamming effects in IRS assisted UAV dual-hop FSO communication systems. IEEE Transactions on Vehicular Technology, 72(7), 8956-8971. https://doi.org/10.1109/TVT.2023.3246817

Zargari, S., Hakimi, A., Tellambura, C., & Herath, S. (2022). User scheduling and trajectory optimization for energy-efficient IRS-UAV networks with SWIPT. IEEE Transactions on Vehicular Technology, 72(2), 1815-1830. https://doi.org/10.1109/TVT.2022.3207700

Wang, Y., Liu, R., Yuan, J., Lu, J., Wang, Z., Wu, R., et al. (2024). Performance Analysis of UAV-IRS Relay Multi-Hop FSO/THz Link. Electronics, 13(16), 3247. https://doi.org/10.3390/electronics13163247

Ahmed, M., Alshahrani, H. M., Alruwais, N., Asiri, M. M., Al Duhayyim, M., Khan, W. U., & Nauman, A. (2023). Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networks. Journal of King Saud University-Computer and Information Sciences, 35(8), 101646. https://doi.org/10.1016/j.jksuci.2023.101646

Wang, X. (2024). Edge Computing Based Multi-Objective Task Scheduling Strategy for UAV with Limited Airborne Resources. Informatica, 48(2), 255-268. https://doi.org/10.31449/inf.v48i2.5885

Zhang, G., & Zhang, J. (2025). High-precision photogrammetric 3d modeling technology based on multi-source data fusion and deep learning-enhanced feature learning using internet of things big data. Informatica, 49(11), 1-16. https://doi.org/10.31449/inf.v49i11.7137

Pang, X., Mei, W., Zhao, N., & Zhang, R. (2022). Intelligent reflecting surface assisted interference mitigation for cellular-connected UAV. IEEE Wireless Communications Letters, 11(8), 1708-1712. https://doi.org/10.1109/LWC.2022.3175920




DOI: https://doi.org/10.31449/inf.v49i21.9892

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.