本文研究了在IRS 輔助的無人機（UAV）無線充能和通訊系統中，對無人機軌跡、IoT 設備分群、功率控制和波束成型的優化。目標是在為所有IoT 設備充電的情況下，最小化數據收集所需的時間。我們將這個非凸最小化問題分解為四個子問題，並使用交替優化算法來優化波束成型和功率控制。在每個群集的上行階段，使用NOMA 傳輸技術，其中消耗的時間由群集中上傳時間最長的設備決定。在下行階段，UAV 為設備提供無線充能，同樣地，所需時間由充電效率最低的設備決定。我們提出的方案平衡了UAV 在服務設備和飛行時所花費的時間，確定分群策略並解決群內的最小化最大值問題。通過利用NOMA 的特性，我們提出了一個功率控制和軌跡優化的貪婪算法。模擬結果表明，該方案能夠有效地在上行、下行和飛行時間中進行權衡，顯著縮短整體任務執行週期。

This paper investigates the optimization of UAV trajectory, IoT device clustering,power control, and beamforming in an IRS-assisted UAV wireless power transfer and communication system. The objective is to minimize the time required for data collection while simultaneously charging all IoT devices. We decompose this non-convex minimization problem into four sub-problems and employ an alternating optimization algorithm to optimize beamforming and power control.
During the uplink phase of each cluster, NOMA transmission technology is utilized,where the time consumed is dominated by the device with the longest upload time in the cluster. In the downlink phase, the UAV provides wireless power transfer to the devices, and similarly, the time taken is determined by the device with the lowest charging efficiency.
Our proposed scheme balances the time the UAV spends on servicing devices and traveling. It determines the clustering strategy and addresses the intra-cluster minimax problem. By leveraging the properties of NOMA, we introduce a greedy algorithm for power control and trajectory optimization. Simulation results demonstrate that our scheme effectively balances uplink, downlink, and flight times, significantly reducing the overall mission execution cycle.

[1] T. Wang, F. Fang, and Z. Ding, “An sca and relaxation based energy efficiency optimization for multi-user ris-assisted noma networks,” IEEE Transactions on Vehicular Technology, vol. 71, no. 6, pp. 6843–6847, 2022.
[2] K. P. Valavanis and G. J. Vachtsevanos, Handbook of unmanned aerial vehicles. Springer Publishing Company, Incorporated, 2014.
[3] K. K. Nguyen, T. Q. Duong, T. Do-Duy, H. Claussen, and L. Hanzo, “3d uav trajectory and data collection optimisation via deep reinforcement learning,” IEEE Transactions on Communications, vol. 70, no. 4, pp. 2358–2371, 2022.
[4] Y. Wang, Z. Su, N. Zhang, and R. Li, “Mobile wireless rechargeable uav networks: Challenges and solutions,” IEEE Communications Magazine, vol. 60, no. 3, pp. 33–39, 2022.
[5] Q. Wu, G. Y. Li, W. Chen, D. W. K. Ng, and R. Schober, “An overview of sustainable green 5g networks,” IEEE Wireless Communications, vol. 24, no. 4, pp. 72–80, 2017.
[6] S. Bi, C. K. Ho, and R. Zhang, “Wireless powered communication: opportunities and challenges,” IEEE Communications Magazine, vol. 53, no. 4, pp. 117–125, 2015.
[7] N. M. Tran, M. M. Amri, J. H. Park, D. I. Kim, and K. W. Choi, “Reconfigurableintelligent-surface-aided wireless power transfer systems: Analysis and implementation,”IEEE Internet of Things Journal, vol. 9, no. 21, pp. 21 338–21 356, 2022.
[8] Y. Wang, M. Chen, C. Pan, K. Wang, and Y. Pan, “Joint optimization of uav trajectory and sensor uploading powers for uav-assisted data collection in wireless sensor networks,” IEEE Internet of Things Journal, vol. 9, no. 13, pp. 11 214–11 226, 2022.
[9] B. Zhu, E. Bedeer, H. H. Nguyen, R. Barton, and Z. Gao, “Uav trajectory planning for aoi-minimal data collection in uav-aided iot networks by transformer,” IEEE Transactions on Wireless Communications, vol. 22, no. 2, pp. 1343–1358, 2023.
[10] R. Ebendt and R. Drechsler, “Weighted a search – unifying view and application,”Artificial Intelligence, vol. 173, no. 14, pp. 1310–1342, 2009. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S000437020900068X
[11] M. Javad-Kalbasi, M. S. Al-Abiad, and S. Valaee, “Energy efficient communications in ris-assisted uav networks based on genetic algorithm,” in GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023, pp. 5901–5906.
[12] X. Liu, Y. Yu, F. Li, and T. S. Durrani, “Throughput maximization for risuav relaying communications,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 19 569–19 574, 2022.
[13] S. K. Singh, K. Agrawal, K. Singh, C.-P. Li, and Z. Ding, “Noma enhanced hybrid ris-uav-assisted full-duplex communication system with imperfect sic and csi,”IEEE Transactions on Communications, vol. 70, no. 11, pp. 7609–7627, 2022.
[14] Y. Liu, S. Wang, L. Ma, Y. Huang, and W. Xing, “Energy-efficient ris-uav relay with trajectory optimization and fair communication,” in 2022 IEEE 8th international Conference on Computer and Communications (ICCC), 2022, pp. 339–343.
[15] K. K. Nguyen, A. Masaracchia, V. Sharma, H. V. Poor, and T. Q. Duong, “Risassisted uav communications for iot with wireless power transfer using deep reinforcement learning,” IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 5, pp. 1086–1096, 2022.
[16] D. Song, W. Shin, and J. Lee, “A maximum throughput design for wireless powered communication networks with irs-noma,” IEEE Wireless Communications Letters, vol. 10, no. 4, pp. 849–853, 2021.
[17] X. Mu, Y. Liu, L. Guo, J. Lin, and L. Hanzo, “Noma-aided joint radar and multicast-unicast communication systems,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 6, pp. 1978–1992, 2022.
[18] Z.-q. Luo, W.-k. Ma, A. M.-c. So, Y. Ye, and S. Zhang, “Semidefinite relaxation of quadratic optimization problems,” IEEE Signal Processing Magazine, vol. 27, no. 3, pp. 20–34, 2010.