在最近幾年裡，無人飛行載具（UAVs）已在各個領域及行業中獲得了極廣泛的應用並從中凸顯了它們強大的能力。同時，將UAVs與移動邊緣計算（MEC）伺服器整合起來，已成為在網路邊緣提供計算服務的一種熱門且有建設性的方法。然而，在面對複雜的資源約束的同時，要如何提高此類系統的整體計算能力無庸置疑是會是一大挑戰。所以為了瞭解及解決這一問題及挑戰，本文深入研究了一種UAV輔助的MEC系統，其中UAVs作為空中移動基站來服務並滿足地面上的用戶設備（UEs）的計算需求。我們的最主要目的是透過聯合優化卸載時間的分配、計算資源、UAV的軌跡、充電站（CSs）的佈置和充電決策，在遵守各種資源、能量和無人機本身限制的情況下，來最小化UAV的任務完成時間。鑒於所制定問題的非凸性質，直接求解會因為各種因素變得相當困難，所以我們必須簡化問題，首先我們先將問題轉換為可行性的評估決策。隨後，利用K-means演算法和螞蟻群優化（ACO）演算法來簡化問題，最終將其分解為兩個凸優化子問題各自求解。再利用交替式優化 (AO) 和連續凸近似（SCA）方法來迭代兩個子問題的解直到收斂，最後利用二元搜索來架構出一種高效的迭代演算法，交替解決這些子問題，並確保收斂。廣泛的模擬結果也驗證了我們所提出的聯合優化演算法的有效性，顯示相比其他方案，其能有效找出最佳的完成任務所需總時間。

In recent years, the extensive use of Unmanned Aerial Vehicles (UAVs) in various industries has emphasized their versatility. Meanwhile, integrating UAVs with Mobile Edge Computing (MEC) servers has become a promising approach for providing computing services at the edge of the network. However, it is a challenge to improve the overall computational capability of such systems in the face of complex resource constraints. To address this challenge, this paper investigates a UAV-assisted MEC system in which UAVs act as airborne mobile base stations to fulfill the computational task requirements of ground-based user equipment (UEs). The main objective is to jointly optimize the offloading time allocation, computational resources, UAV trajectories, charging station (CSs) placement and charging decisions to minimize the task completion time of the UAVs while adhering to various resource, energy and speed constraints. Given the non-convex nature of the formulated problem, in order to achieve this objective, the problem is first transformed into a feasibility assessment problem. Subsequently, the problem is simplified using K-means algorithm and Ant Colony Optimization (ACO) algorithm, and finally decomposed into two convex optimization subproblems. An efficient iterative algorithm is proposed to solve these subproblems alternatively by utilizing the Alternating Optimization (AO) and the Successive Convex Approximation (SCA) method to ensure the convergence. Extensive simulation results validate the effectiveness of the proposed joint optimization algorithm, showing its ability to reduce the total service time compared to other schemes.

Z. Xiao, F. Li, H. Jiang, J. Bai, J. Xu, F. Zeng, and M. Liu, “A joint information and energy cooperation framework for cr-enabled macro–femto heterogeneous networks,” IEEE Internet of Things Journal, vol. 7, no. 4, pp. 2828–2839, 2020.
Z. Xiao, X. Dai, H. Jiang, D. Wang, H. Chen, L. Yang, and F. Zeng, “Vehicular task offloading via heat-aware mec cooperation using game-theoretic method,” IEEE Internet of Things Journal, vol. 7, no. 3, pp. 2038–2052, 2020.
L. Zhu, J. Song, X. Zhu, C. Zhang, S. Zhang, and X. Yuan, “Adversarial learning-based semantic correlation representation for cross-modal retrieval,” IEEE MultiMedia, vol. 27, no. 4, pp. 79–90, 2020.
B. Li, Z. Fei, and Y. Zhang, “Uav communications for 5g and beyond: Recent advances and future trends,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2241–2263, 2019.
L. Chettri and R. Bera, “A comprehensive survey on internet of things (iot) toward 5g wireless systems,” IEEE Internet of Things Journal, vol. 7, no. 1, pp. 16–32, 2020.
Z. Xiao, X. Shen, F. Zeng, V. Havyarimana, D. Wang, W. Chen, and K. Li, “Spectrum resource sharing in heterogeneous vehicular networks: A noncooperative game-theoretic approach with correlated equilibrium,” IEEE Transactions on Vehicular Technology, vol. 67, no. 10, pp. 9449–9458, 2018.
A. Fotouhi, H. Qiang, M. Ding, M. Hassan, L. G. Giordano, A. Garcia-Rodriguez, and J. Yuan, “Survey on uav cellular communications: Practical aspects, standardization advancements, regulation, and security challenges,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3417–3442, 2019.
M. Mozaffari, W. Saad, M. Bennis, Y.-H. Nam, and M. Debbah, “A tutorial on uavs for wireless networks: Applications, challenges, and open problems,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2334–2360, 2019.
F. Zeng, Z. Hu, Z. Xiao, H. Jiang, S. Zhou, W. Liu, and D. Liu, “Resource allocation and trajectory optimization for qoe provisioning in energy-efficient uav-enabled wireless networks,” IEEE Transactions on Vehicular Technology, vol. 69, no. 7, pp. 7634–7647, 2020.
P. Mach and Z. Becvar, “Mobile edge computing: A survey on architecture and computation offloading,” IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1628–1656, 2017.
N. Cheng, W. Xu, W. Shi, Y. Zhou, N. Lu, H. Zhou, and X. Shen, “Air-ground integrated mobile edge networks: Architecture, challenges, and opportunities,” IEEE Communications Magazine, vol. 56, no. 8, pp. 26–32, 2018.
W. Zhang, L. Li, N. Zhang, T. Han, and S. Wang, “Air-ground integrated mobile edge networks: A survey,” IEEE Access, vol. 8, pp. 125 998–126 018, 2020.
Q. Liu, L. Shi, L. Sun, J. Li, M. Ding, and F. Shu, “Path planning for uav-mounted mobile edge computing with deep reinforcement learning,” IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5723–5728, 2020.
J.-S. Lee and K.-H. Yu, “Optimal path planning of solar-powered uav using gravitational potential energy,” IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 3, pp. 1442–1451, 2017.
Y. Zeng, R. Zhang, and T. J. Lim, “Wireless communications with unmanned aerial vehicles: opportunities and challenges,” IEEE Communications Magazine, vol. 54, no. 5, pp. 36–42, 2016.
Y. Wang, H. Wang, and X. Wei, “Energy-efficient uav deployment and task scheduling in multi-uav edge computing,” in 2020 International Conference on Wireless Communications and Signal Processing (WCSP), 2020, pp. 1147–1152.
J. Xiong, H. Guo, and J. Liu, “Task offloading in uav-aided edge computing: Bit allocation and trajectory optimization,” IEEE Communications Letters, vol. 23, no. 3, pp. 538–541, 2019.
Y. Qian, F. Wang, J. Li, L. Shi, K. Cai, and F. Shu, “User association and path planning for uav-aided mobile edge computing with energy restriction,” IEEE Wireless Communications Letters, vol. 8, no. 5, pp. 1312–1315, 2019.
L. Lyu, F. Zeng, Z. Xiao, C. Zhang, H. Jiang, and V. Havyarimana, “Computation bits maximization in uav-enabled mobile-edge computing system,” IEEE Internet of Things Journal, vol. 9, no. 13, pp. 10 640–10 651, 2022.
H. Y. Alsheyab, E. Bedeer, S. Choudhury, and S. Ikki, “Multi-uav wireless networks: Jointly trajectory optimization and resource allocation,” in 2022 IEEE International Conference on Communications Workshops (ICC Workshops), 2022, pp. 1053–1058.
X. Cao, J. Xu, and R. Zhang, “Mobile edge computing for cellular-connected uav: Computation offloading and trajectory optimization,” in 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018, pp. 1–5.
X. Zhang, Z. Chang, G. Zhang, M. Li, and Y. Hu, “Trajectory optimization and resource allocation for time minimization in the uav-enabled mec system,” in 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022, pp. 333–338.
Y. Guo, S. Yin, and J. Hao, “Resource allocation and 3-d trajectory design in wireless networks assisted by rechargeable uav,” IEEE Wireless Communications Letters, vol. 8, no. 3, pp. 781–784, 2019.
J. Chen and J. Xie, “Joint task scheduling, routing, and charging for multi-uav based mobile edge computing,” in ICC 2022 - IEEE International Conference on Communications, 2022, pp. 1–6.
J. Liu, H. Guo, J. Xiong, N. Kato, J. Zhang, and Y. Zhang, “Smart and resilient ev charging in sdn-enhanced vehicular edge computing networks,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 1, pp. 217–228, 2020.
M. Li, L. Liu, J. Xi, Z. Ma, X. Xu, and L. Wang, “Ectsa: An efficient charging time scheduling algorithm for wireless rechargeable uav network,” in 2021 IFIP Networking Conference (IFIP Networking), 2021, pp. 1–9.
Y. Ma, Y. Tang, Z. Mao, D. Zhang, C. Yang, and W. Li, “Energy-efficient 3d trajectory optimization for uav-aided wireless sensor networks,” in GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023, pp. 6591–6596.
B. Duo, Q. Wu, X. Yuan, and R. Zhang, “Anti-jamming 3d trajectory design for uav-enabled wireless sensor networks under probabilistic los channel,” IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 16 288–16 293, 2020.
C. Zhan, H. Hu, X. Sui, Z. Liu, and D. Niyato, “Completion time and energy optimization in the uav-enabled mobile-edge computing system,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7808–7822, 2020.
Y. Zeng, J. Xu, and R. Zhang, “Energy minimization for wireless communication with rotary-wing uav,” IEEE transactions on wireless communications, vol. 18, no. 4, pp. 2329–2345, 2019.
M. Grant and S. Boyd, “Cvx: Matlab software for disciplined convex programming, version 2.1,” 2014.
S. P. Boyd and L. Vandenberghe, Convex optimization. Cambridge university press, 2004.
M. Hong, X. Wang, M. Razaviyayn, and Z.-Q. Luo, “Iteration complexity analysis of block coordinate descent methods,” Mathematical Programming, vol. 163, pp. 85–114, 2017.