Learning to Deployment: Data-Driven On-Demand UAV Placement for Throughput Maximization
Leiyu Wang, Haixia Zhang, Shuaishuai Guo, Dongyang Li, Dongfeng Yuan
Abstract
Unmanned Aerial Vehicles (UAVs) communications appear to be one of the most promising paradigms for future wireless communication networks, because of their high flexibility in providing on-demand communication services. In this context, this paper investigates a fast and fine-grained UAV deployment scheme so as to improve the network throughput and meet the real-time communication demands of users. The key novelty of the proposed scheme lies in that the UAV deployment problem is formulated as a computer vision problem and a novel UAV deployment method, i.e., a convolutional neural network (CNN)-based UAV deployment method is proposed to solve it. By taking advantage of the classical CNN models such as VGG-Net, AlexNet, the UAV deployment position can be determined timely. Compared with the existing work, this work not only reduces the computational time overhead for determining the deployment positions of UAV, but also shortens the deployment response time of UAV. The superiority of the proposed UAV deployment scheme is investigated and verified. Simulation results demonstrate that the proposed scheme can provide a fast and on-demand UAV deployment solution while guaranteeing the throughput performance of the UAV network.