Minimal Throughput Maximization of UAV-enabled Wireless Powered Communication Network in Cuboid Building Perimeter Scenario

• Published in: IEEE eTransactions on network and service management Vol. 20; no. 4; p. 1
• Main Authors: Tang, Qiang, Yang, Yong, Liu, Lixin, Yang, Kun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous aerial vehicles; bottleneck-awared; Buildings; Communications networks; Complexity; Game theory; Hovering; Internet of Things; Mixed integer; Nonlinear programming; Optimization; Particle swarm optimization; Resource management; Sensors; Structural health monitoring; Trajectory; unmanned aerial vehicle; Unmanned aerial vehicles; Wireless communication; Wireless communications; Wireless power transmission; wireless powered communication network; Wireless sensor networks
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2023.3268634

As the number of Internet of Things Devices (IoTDs) increases, the building Structural Health Monitoring (SHM) system is subject to the enormous amount of data collected from sensors. To tackle this challenge, we investigate an Unmanned Aerial Vehicle (UAV)-enabled Wireless Powered Communication Network (WPCN) in a building SHM scenario where a UAV is dispatched to provide wireless charging and data relaying services for IoTDs on the building. For preventing the channel blockage caused by the building, we place the UAV and Access Points (APs) in specific trajectory and locations, respectively. To improve the system's throughput, we maximize the minimum data volume among devices in a given period by formulating an optimization problem in which we jointly optimize the link schedule, the power and time allocation and the hovering positions of the UAV. However, the formulated problem is a mixed-integer nonlinear programming and is hard to solve. Therefore, we adopt a bottleneck-aware idea to reduce the dimensionality of the optimization variables in order to obtain a simplified problem that can be solved in a low-complexity way. Also, the Block Coordinate Descent (BCD) method is applied to reduce the complexity of the problem. Meanwhile, we further propose a method to deal with the heterogeneous problem for improving the generalizability of our algorithm. To estimate the performance of our proposed algorithm, we compare it with the Monte Carlo (MC) method, Game Theory (GT) and Particle Swarm Optimization (PSO). The simulation results indicate that our algorithm can obtain better performance.