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Network planning and resource management for UAV-based wireless networks.


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Nguyen, Minh Dat (2022). Network planning and resource management for UAV-based wireless networks. Thèse. Québec, Doctorat en télécommunications, Université du Québec, Institut national de la recherche scientifique, 254 p.

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Next-generation wireless networks will enable to support applications in various domains including smart factories, intelligent transportation, e-health, and more. Therefore, future wireless communications are expected to provide higher capacity and much lower latency and offer excellent stability, ubiquitous communications, and connectivity to billions of devices. However, the deployment of terrestrial infrastructure faces challenges in various practical scenarios, such as communications to serve temporary events and emergencies like natural disasters and fast service recovery. Toward this end, several promising technologies have been under consideration, including satellite communications, unmanned aerial vehicle (UAV) communications, intelligent reflecting surface (IRS), and mobile edge computing (MEC). The overall objective of this Ph.D. research is to develop network planning and resource management for UAV-based wireless networks. Our research has resulted in three major research contributions, which are presented in three corresponding main chapters of this dissertation. First, we study the trajectory control, sub-channel assignment, and user association design for UAVs-based wireless networks, which is presented in Chapter 5. In particular, we propose a method to optimize the max-min average rate subject to data demand constraints of ground users (GUs) where spectrum reuse and co-channel interference management are considered. The mathematical model is a mixed integer non-linear optimization problem which we solve by using the alternating optimization approach where we iteratively optimize the user association, sub-channel assignment, and UAV trajectory control until convergence. For the sub-channel assignment sub-problem, we propose an iterative sub-channel assignment (ISA) algorithm to obtain an efficient solution. Moreover, the successive convex approximation (SCA) is used to convexify and solve the non-convex UAV trajectory control sub-problem. Second, we design an UAV-based wireless network with wireless access and backhaul links leveraging an IRS, which is covered in Chapter 6. Particularly, this design aims to maximize the sum rate achieved by GUs through optimizing the UAV placement, IRS phase shifts, and sub-channel assignments considering the wireless backhaul capacity constraint. To tackle the underlying mixed integer non-linear optimization problem (MINLP), we first derive the closed-form IRS phase shift solution; we then optimize the sub-channel assignment and UAV placement by using the alternating optimization method. Specifically, we propose an iterative sub-channel assignment method to efficiently utilize the bandwidth and balance bandwidth allocation for wireless access and backhaul links while maintaining the backhaul capacity constraint. Moreover, we employ the successive convex approximation (SCA) method to solve the UAV placement optimization sub-problem. Finally, we study the computation offloading problem in space-air-ground integrated networks (SAGIN), where joint optimization of partial computation offloading, UAV trajectory control, user scheduling, computation, resource allocation, and admission control is performed. The research outcomes of this study are presented in Chapter 7. Specifically, the considered SAGIN employs multiple UAV-mounted edge servers with controllable UAV trajectory and a cloud sever which can be reached by GUs via multi-hop low-earth-orbit (LEO) satellite communications. This design aims to minimize the weighted energy consumption of the GUs and UAVs while satisfying the maximum delay constraints of underlying computation tasks. To tackle the underlying non-convex mixed integer non-linear optimization problem, we use the alternating optimization approach where we iteratively solve four subproblems, namely user scheduling, partial offloading control and bit allocation over time slots, computation resource and bandwidth allocation, and multi-UAV trajectory control until convergence. Moreover, feasibility verification and admission control strategies are proposed to handle overloaded network scenarios. Furthermore, the successive convex approximation (SCA) method is employed to convexify and solve the non-convex computation resource and bandwidth allocation and UAV trajectory control sub-problems. For all proposed designs and algorithms, we provide extensive analytical and numerical studies which illustrate their achievable performances as the values of different key parameters vary. The numerical studies also demonstrate the efficacy of our proposed algorithms and their significant performance gains versus the state-of-the-art designs.

Type de document: Thèse Thèse
Directeur de mémoire/thèse: Le, Long Bao
Co-directeurs de mémoire/thèse: Girard, André
Mots-clés libres: télécommunication
Centre: Centre Énergie Matériaux Télécommunications
Date de dépôt: 30 mars 2023 19:23
Dernière modification: 30 mars 2023 19:23
URI: https://espace.inrs.ca/id/eprint/13243

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