Resource Allocation and Trajectory Optimization for QoE Provisioning in Energy-Efficient UAV-Enabled Wireless Networks

• Published in: IEEE transactions on vehicular technology Vol. 69; no. 7; pp. 7634 - 7647
• Main Authors: Zeng, Fanzi, Hu, Zhenzhen, Xiao, Zhu, Jiang, Hongbo, Zhou, Siwang, Liu, Wenping, Liu, Daibo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-07-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Base stations; Computer simulation; Energy consumption; Energy efficiency; Energy-efficient; Iterative algorithms; Optimization; Power; Provisioning; Quality of experience; Resource allocation; Scheduling; Trajectory; Trajectory optimization; UAV trajectory optimization; Unmanned aerial vehicles; user quality-of-experience; User requirements; Wireless communication; Wireless communication systems; Wireless communications; Wireless networks
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2020.2986776

In the past several years, unmanned aerial vehicle (UAV) have been employed to provide enhanced coverage or relay service to mobile users in a scenario with limited or even no infrastructure since they can be deployed to almost everywhere and can be manipulated at anytime. This paper studies UAV as aerial base station (BS) enabled wireless communication system, where a UAV is dispatched to provide wireless communication service to a set of ground users with difference quality-of-experience (QoE) requirements. In real world, user requirements are randomly and unevenly distributed. In addition, UAV communication coverage and on-board energy are limited and system resources are also limited (e.g., transmission power, spectrum). In order to meet the QoE of all users with limited system resources and limited UAV energy, we jointly optimize user communication scheduling, UAV trajectory, transmit power and bandwidth allocation to maximize energy-efficiency and satisfy user QoE requirement. The formulated problem is mixes integer non-convex and non-concave so it is difficult to solve. In this paper, we solvevv the problem with two steps as follows. Firstly, we transform the objective function into a tractable form. Secondly, we divide the optimal problem into four sub-optimal problems, and then use a powerful iterative algorithm with the Dinkelbach and block coordinate descent to solve the optimal problem. That is to say, the user scheduling, UAV trajectory, transmission power and bandwidth allocation are alternately optimized in each iteration. Extensive simulation results present that our proposed method can obtain higher energy efficiency than that of baselines. Specifically, the energy efficiency obtained by our proposed method is 12.5% higher than the approach that only maximizes throughput.