Research Article
Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications
@INPROCEEDINGS{10.1007/978-3-030-69066-3_23, author={Shuo Zhang and Xuemai Gu and Shuo Shi}, title={Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications}, proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings}, proceedings_a={AICON}, year={2021}, month={7}, keywords={Unmanned aerial vehicles Trajectory planning K-means algorithm}, doi={10.1007/978-3-030-69066-3_23} }- Shuo Zhang
Xuemai Gu
Shuo Shi
Year: 2021
Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications
AICON
Springer
DOI: 10.1007/978-3-030-69066-3_23
Abstract
Unmanned aerial vehicles (UAV) has become a popular auxiliary method in the communication field due to its mobility and mobility. The air base station (BS) is one of the important roles of UAV. It can serve the ground terminals (GTs) without being restricted by time and space. When GTs are scattered, trajectory optimization becomes an indispensable part of the UAV communication. In this paper, we consider a UAV-assisted network with underlaid D2D users (DUs), where the UAV aims to achieve full coverage of DUs. Trajectory planning is transformed into the deployment and connection of UAV stop points (SPs), and a K-means-based trajectory planning algorithm is proposed. By clustering DUs, the initial SPs is determined. Then add new SPs according to the coverage, and construct the trajectory. The simulation analyzes the validity of the algorithm from the distribution of DUs and the number of initial cluster centers. The results show that the proposed algorithm is compared favorably against well-known benchmark scheme in terms of the length of the trajectory.