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研究生： 劉晉維
Liu, Jin-Wei
論文名稱： 部分卸載的群聚網路之無人機行動邊緣運算方案
UAV-Enabled Mobile Edge Computing for Partially Offloaded Cluster Network
指導教授： 張志文
Chang, Wenson
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電腦與通信工程研究所
Institute of Computer & Communication Engineering
論文出版年： 2026
畢業學年度： 114
語文別： 英文
論文頁數： 63
中文關鍵詞： 無人機 、行動邊緣運算 、叢集 、密集網路 、部分卸載
外文關鍵詞： UAV, MEC, clustering, dense network, partial offloading
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    • 在本文中，我們研究了支持部分卸載、群集和無人機（UAV）的行動邊緣運算(Mobile Edge Computing, MEC)計算網絡的結合。在此架構中，使用者裝置(UE)可以將自身的計算任務分成本地運算，以及透過叢集頭(CH)轉送至服務該群的無人機兩個部分，進而降低整個系統的功率消耗。本論文的目標是在滿足包含使用者本地計算、第一跳傳輸，以及第二跳傳輸與無人機計算之端到端延遲限制下，最小化系統的功率消耗。要最佳化這個非凸的問題，本文中將其拆解成四個子問題去求解：（1）決定UE的計算任務卸載比例；（ 2）UE傳輸計算任務的傳輸功率；（3）UAV分給服務的每個群的計算資源；以及（4）每個UAV的位置（包括其高度）。 基於模擬結果，UE在可以部份卸載任務的情況下會比傳統的只能全卸載或純本地運算的情況有較低的功率消耗。

    In this paper, we research the integration of partial offloading, clustering, and unmanned aerial vehicles (UAVs) in a mobile edge computing (MEC) network. In the proposed model, each user equipment (UE) may divide its computation task into a locally executed portion and a portion that is forwarded through its cluster head (CH) to the UAV assigned to that cluster, thereby reducing the total power consumption of the system. The goal of this paper is to reduce the overall system power consumption while satisfying end-to-end latency constraints across local computing, hop-1 transmission, and hop-2 transmission and UAV computing. To address this non-convex optimization problem, we divide it into four subproblems: (1) determining the proportion of computation tasks to be offloaded by each UE; (2) determining the transmission power for UEs to offload their tasks; (3) allocating the UAV’s computation resources to each cluster; and (4) determining the location (including the altitude) of each UAV. Based on simulation results, when UEs are allowed to partially offload their tasks, the system achieves lower power consumption compared with the cases where UEs must either fully offload or compute locally.

    Chinese Abstract i English Abstract ii Acknowledgements iii Contents iv List of Tables vi List of Figures vii List of Variables viii List of Acronyms x 1 Introduction 1 1.1 Research Background 1 1.2 Motivation 1 1.3 Problem Statement 2 1.4 Contributions 3 1.5 Thesis Organization 3 2 Literature Survey 5 2.1 Clustering in UAV-Assisted MEC 5 2.2 Partial Offloading Mechanisms 6 2.3 UAV Placement and Trajectory Optimization 8 2.4 Multi-UAV and Multi-Hop Systems 10 2.5 Summary 11 3 Background Knowledge 13 3.1 Wireless Channel Models 13 3.2 Half-Duplex (HD) and Full-Duplex (FD) 14 3.2.1 Half-Duplex (HD) 15 3.2.2 Full-Duplex (FD) 15 3.3 Mobile Edge Computing (MEC) 15 3.4 Clustering 16 4 System Model 18 4.1 Network Geometry and Notation 18 4.2 UE Local Computing 20 4.3 UE-to-CH and CH-to-UAV Transmission Time 21 4.4 UAV Computation Model 23 4.5 Total Latency Model 25 5 Problem Formulation and Proposed Solution Scheme 27 5.1 Problem Formulation 27 5.2 Proposed Solution Method 29 5.2.1 Subproblem of Offloading Decisions λ 30 5.2.2 Subproblem of Power Allocation p 30 5.2.3 Subproblem of Computing Allocation f 31 5.2.4 Unified PSO Framework for λ, p, and f 32 5.2.5 Subproblem of UAV Deployment z 34 5.2.6 Overall System Optimization Framework 36 5.2.7 Remarks and Discussion 38 6 Simulation Results and Discussion 40 7 Conclusion and Future Work 46 7.1 Conclusion 46 7.2 Future Work 47 Bibliography 48 Appendix A 51

    [1] D. Malak, H. S. Dhillon, and J. G. Andrews, “Optimizing Data Aggregation for Uplink Machine-to-Machine Communication Networks,” IEEE Transactions on Communications, vol. 64, no. 3, pp. 1274– 1290, 2016.
    [2] S. Alfattani, W. Jaafar, H. Yanikomeroglu, and A. Yongacoglu, “Multi-UAV Data Collection Framework for Wireless Sensor Networks,” IEEE Global Communications Conference, 2019.
    [3] U. Saleem, Y. Liu, S. Jangsher, X. Tao, and Y. Li, “Latency Minimization for D2D-Enabled Partial Computation Offloading in Mobile Edge Computing,” IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4472– 4486, 2020.
    [4] H. Tang, H. Wu, Y. Zhao, and R. Li, “Joint Computation Offloading and Resource Allocation Under Task-Overflowed Situations in Mobile-Edge Computing,” IEEE Transactions on Network and Service Management, vol. 19, no. 2, pp. 1539– 1553, 2022.
    [5] Q. Hu, Y. Cai, G. Yu, Z. Qin, M. Zhao, and G. Y. Li, “Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1879– 1892, 2019.
    [6] Y. Wang, H. Wang, and X. Wei, “Energy-Efficient UAV Deployment and Task Scheduling in Multi-UAV Edge Computing,” in IEEE Transactions on Vehicular Technology, 2020.
    [7] X. He, R. Jin, and H. Dai, “Multi-Hop Task Offloading With On-the-Fly Computation for Multi-UAV Remote Edge Computing,” IEEE Transactions on Communications, vol. 70, no. 2, pp. 1332– 1344, 2022.
    [8] X. Chen, Y. Bi, G. Han, D. Zhang, M. Liu, and H. Shi, “Distributed Computation Offloading and Trajectory Optimization in Multi-UAV-Enabled Edge Computing,” IEEE Internet of Things Journal, vol. 9, no. 20, pp. 20096– 20110, 2022.
    [9] Z. Kuang, L. Li, J. Gao, L. Zhao, and A. Liu, “Partial Offloading Scheduling and Power Allocation for Mobile Edge Computing Systems,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6774– 6785, 2019.
    [10] J. Bi, H. Yuan, S. Duanmu, M. Zhou, and A. Abusorrah, “Energy-Optimized Partial Computation Offloading in Mobile-Edge Computing with Genetic Simulated Annealing-Based Particle Swarm Optimization,” IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3774– 3785, 2021.
    [11] J. Kwak, Y. Kim, J. Lee, and S. Chong, “DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 12, pp. 2510– 2523, 2015.
    [12] Z. Yu, Y. Gong, S. Gong, and Y. Guo, “Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing,” IEEE Internet of Things Journal, vol. 7, no. 4, pp. 3147– 3159, 2020.
    [13] Y. Li and L. Li, “Computation Offloading and Resource Allocation in MEC enabled Vehicular Networks: Partial Offloading Versus Binary Offloading,” in 2024 7th World Conference on Computing and Communication Technologies, 2024.
    [14] F. Zhou, Y. Wu, H. Sun, and Z. Chu, “UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design,” in IEEE International Conference on Communications, 2018.
    [15] 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.
    [16] Y. Liu, K. Xiong, Q. Ni, P. Fan, and K. Ben Letaief, “UAV-Assisted Wireless Powered Cooperative Mobile Edge Computing: Joint Offloading, CPU Control, and Trajectory Optimization,” IEEE Internet of Things Journal, vol. 7, no. 4, pp. 2777– 2790, 2020.
    [17] Z. Liao, Y. Ma, J. Huang, J. Wang, and J. Wang, “HOTSPOT: A UAV-Assisted Dynamic Mobility-Aware Offloading for Mobile-Edge Computing in 3-D Space,” IEEE Internet of Things Journal, vol. 8, no. 13, pp. 10940– 10952, 2021.
    [18] X. Hu, K.-K. Wong, K. Yang, and Z. Zheng, “Task and Bandwidth Allocation for UAV-Assisted Mobile Edge Computing with Trajectory Design,” in 2019 IEEE Global Communications Conference, 2019.
    [19] Z. Yang, C. Pan, K. Wang, and M. Shikh-Bahaei, “Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks,” IEEE Transactions on Wireless Communications, vol. 18, no. 9, pp. 4576– 4589, 2019.
    [20] T. Do-Duy, L. D. Nguyen, T. Q. Duong, S. R. Khosravirad, and H. Claussen, “Joint Optimisation of Real-Time Deployment and Resource Allocation for UAV aided Disaster Emergency Communications,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 11, pp. 3411– 3424, 2021.

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