本篇論文研究了無人機在中繼通訊和影像監視中的應用，我們使用多架影像監視無人機，研究地面用戶和遠程基地台 (BS) 之間的中繼通訊，無人機將監控區域的航拍影像轉發給基地台，同時滿足地面用戶的上行傳輸需求。我們首先考慮單無人機場景，在受監視覆蓋範圍、影像傳輸要求和中繼容量的約束，透過最大化用戶的總和對數吞吐量來決定無人機的軌跡、任務分配、用戶關聯和速率分配。我們將此一問題表示成混合整數非線性規劃問題，並利用不精確區塊坐標下降 (Block Coordinate Decent, BCD) 演算法解決此一問題。在BCD算法中我們繼承了具有平衡約束的數學規劃的精確懲罰方法的概念，以放寬整數約束和逐次凸逼近方法來解決非凸性的軌跡優化問題。然後，我們將所提出的框架擴展到具有多個無人機的情況，這些無人機被派往覆蓋廣泛的監視區域。無人機可以透過協作和適當的任務分配，更高效地完成中繼和監視任務，我們採用相似的 BCD 演算法來解決多無人機的問題。

除此之外，我們研究影像監視無人機的任務分配、傳輸調度和軌跡設計，這些無人機為地面用戶提供即時影像捕獲和交付有時效性的服務。例如，尋求交通壅塞影像的駕駛或請求私人住宅影像的保全單位。在每項任務中，無人機都需要拍攝指定監控區域的影像，並在截止時間前將影像交付給請求用戶，並確定任務分配、傳輸調度和軌跡設計，以最大化已完成任務的總監視區域。在這個問題，我們首先研究單無人機，並提出一種交替優化方法，該方法採用精確懲罰方法來完成近似二元解，並採用逐次凸逼近來處理非凸軌跡優化。我們也擴展酖無人機到多無人機場景，其中跨無人機任務可能需要由不同無人機拍攝和傳送影像。為了實現分散式演演算法，我們引入輔助期限來限制本地任務的可用時間，從而將聯合優化問題解耦成為多個單無人機問題，這些問題可以按照前一案例中推導出的程序平行處理。最後，我們提供了數值模擬來證明以上解決方法的有效性。

This work studies the employment of UAVs in relay communications and image surveillance. We examine the use of image surveillance UAVs for relay communication between ground users and a remote base station (BS). UAVs take aerial images of the surveillance region and forward them to the BS while serving the uplink transmission demands of ground users. We first consider the single-UAV scenario and jointly determine the UAV's trajectory, task assignment, user association, and rate allocation by maximizing the sum-log-throughput of the users subject to constraints on the surveillance coverage, image transmission requirements, and relay capacity. The resulting mixed-integer nonlinear programming problem is solved by an inexact block coordinate descent (BCD) algorithm where we inherit ideas from the exact penalty method for mathematical programming with equilibrium constraints to relax the integer constraints and the successive convex approximation (SCA) approach to address the non-convexity of the trajectory optimization problem. Then, we extend the proposed framework to the case with multiple UAVs dispatched to cover a broad surveillance region. The UAVs may complete both relay and surveillance tasks more efficiently through cooperation and proper task allocation for UAVs. A similar BCD algorithm is adopted to solve the problem of multiple-UAV case.

We further examine the task assignment, transmission scheduling, and trajectory design of image-surveillance UAVs dispatched to serve on-demand image capture and delivery services to ground users, e.g., from drivers seeking images of traffic jams or security units requesting pictures of private homes. In each task, the UAVs must capture the image of a specified surveillance region and deliver the image to the requesting user before the deadline. The task assignment, transmission scheduling, and trajectory design are jointly determined to maximize the total surveillance area of the completed tasks. In this problem, we first examine the single-UAV problem and propose an alternating optimization approach that adopts the exact penalty method to promote near-binary solutions and employs the SCA method to deal with the non-convex trajectory optimization. We then extend to the multiple-UAV scenario where cross-UAV tasks may require images to be captured and delivered by different UAVs. To enable distributed implementation, we introduce auxiliary deadlines to limit the time available for local tasks and, thus, decouple the joint optimization problem into multiple single-UAV problems that can be solved in parallel following the procedure derived in the previous case. Numerical simulations are provided to demonstrate the effectiveness of the proposed solutions.

Contents
Abstract (Chinese) i
Abstract iii
Acknowledgements v
Contents vi
List of Figures ix
List of Abbreviations xi
List of Symbols xii
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4
1.3 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
2. Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8
3. UAV Trajectory Optimization and Resource Allocation for Joint Relay Communication and Image Surveillance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1 System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Joint Optimization of UAV Trajectory, Task Assignment, and User Association via Inexact Block Coordinate Descent . . . . . . . . . . . . .. . . . . . . . . . . . 21
3.2.1 Subproblem I: UAV Trajectory and Task Assignment Optimization . . . . 23
3.2.2 Subproblem II: User Association Optimization . . . . . . . . . . . . . . . . . . . . . . .27
3.2.3 Subproblem III: Penalty Variables Update . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 Extension to the case with Multiple Cooperative UAVs . . . . . . . . . . . . . . . . . 30
3.3.1 Subproblem I: Multiple-UAV Trajectory and Task Assignment
Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36
3.3.2 Subproblem II: Multiple-UAV User Association Optimization. . . . . . . . . 39
3.3.3 Subproblem III: Multiple-UAV Penalty Variables Update . . . . . . .. . . . . . . 40
3.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41
3.4.1 Experiments for the Single-UAV Case . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . 41
3.4.2 Experiments for the Multiple-UAV Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4. UAV Trajectory Optimization and Resource Allocation for On-Demand Image Capture and Wireless Delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.1 System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.1.1 Image Surveillance Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.1.2 Communications Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.1.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .57
4.2 Joint UAV Trajectory, Task Assignment, and Transmission Scheduling
Algorithm for the Single-UAV Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .58
4.2.1 Subproblem I: Task Assignment & Transmission Scheduling . . . . . . . . . 59
4.2.2 Subproblem II: Task Assignment, UAV Trajectory & Rate Optimization .60
4.2.3 Subproblem III: Penalty Variables Update . . . . . . . . . . . . . . . . . . . . . . . . . . . .62
4.3 Extension to the Multiple-UAV Scenario with UAV Relaying . . . . . . . . . . . . 65
4.3.1 System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.3.2 Proposed Decentralized Solution for the Multiple-UAV Problem . . . . . .69
4.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .74
4.4.1 Results for the Single-UAV Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.4.2 Results for the Multiple-UAV case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5. Conclusion and Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .85
5.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .86

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