Network Resource Management in UAV-Assisted Wireless Networks with RF Energy Harvesting

<p dir="ltr">Future sixth-generation (6G) wireless networks are expected to reliably connect billions of devices with diverse requirements. Unmanned aerial vehicles (UAVs) can contribute to meeting the requirements of 6G networks by providing flexible connectivity. UAVs equipped with mobile edge computing (MEC) can enable the migration of computational resources toward the flying platform. However, network resource optimization is critical for improving the overall performance of UAV-MEC networks. Therefore, joint management of communication, computation, caching, and energy resources is vital to take full advantage of UAV-MEC networks.</p><p dir="ltr">Firstly, we conduct a comprehensive survey on optimizing UAV-assisted wireless networks for different objectives, including coverage area, throughput, and energy efficiency. Secondly, we investigate priority-based resource allocation in wireless-powered UAV-assisted networks. We formulate an optimization problem to minimize the charging cost and maximize the number of UAVs charged by charging stations. We then develop a sequential quadratic programming algorithm to solve the problem in polynomial time. The simulation results demonstrate the effectiveness of the proposed work compared with existing solutions.</p><p dir="ltr">Thirdly, we study task offloading in UAV-MEC networks. We formulate an optimization problem to minimize the latency and cost associated with communication, computation, caching, and harvesting resources, and to maximize the number of Internet of Things (IoT) devices served by UAVs. We propose a two-stage heuristic algorithm based on an unsupervised learning algorithm and an iterative rounding algorithm to solve the problem. The simulation results show the effectiveness of the proposed algorithm in UAV-MEC networks when compared with the optimal results.</p><p dir="ltr">Fourthly, we investigate digital twin-assisted task offloading in UAV-MEC networks with energy harvesting. The goal is to minimize latency and maximize the number of associated IoT devices by optimizing UAV placement and associated devices. To solve the formulated problem, we propose a relaxed heuristic algorithm and a difference-of-convex penalty-based algorithm with reduced computational complexity. Through simulations, we demonstrate the effectiveness of these algorithms and validate the benefits of leveraging digital twin technology in UAV-MEC networks.</p><p dir="ltr">Overall, this thesis contributes to optimizing UAV-MEC networks, offering insights into wireless connectivity and user association. These advancements can enhance the performance and sustainability of wireless networks in various applications.</p>