Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications

Shuo Zhang; Xuemai Gu; Shuo Shi

doi:10.1007/978-3-030-69066-3_23

Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications

Shuo Zhang
, Xuemai Gu
, Shuo Shi^*

^*Corresponding author for this work

School of Electronics and Information Engineering, Harbin Institute of Technology
International Innovation Institute of HIT in Huizhou
Peng Cheng Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Artificial Intelligence for Communications and Networks - 2nd EAI International Conference, AICON 2020, Proceedings
Editors	Shuo Shi, Liang Ye, Yu Zhang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	260-271
Number of pages	12
ISBN (Print)	9783030690656
DOIs	https://doi.org/10.1007/978-3-030-69066-3_23
State	Published - 2021
Externally published	Yes
Event	2nd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2020 - Harbin, China Duration: 19 Dec 2020 → 20 Dec 2020

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	356 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	2nd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2020
Country/Territory	China
City	Harbin
Period	19/12/20 → 20/12/20

Keywords

K-means algorithm
Trajectory planning
Unmanned aerial vehicles

Access to Document

10.1007/978-3-030-69066-3_23

Cite this

Zhang, S., Gu, X., & Shi, S. (2021). Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications. In S. Shi, L. Ye, & Y. Zhang (Eds.), Artificial Intelligence for Communications and Networks - 2nd EAI International Conference, AICON 2020, Proceedings (pp. 260-271). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 356 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-69066-3_23

Zhang, Shuo ; Gu, Xuemai ; Shi, Shuo. / Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications. Artificial Intelligence for Communications and Networks - 2nd EAI International Conference, AICON 2020, Proceedings. editor / Shuo Shi ; Liang Ye ; Yu Zhang. Springer Science and Business Media Deutschland GmbH, 2021. pp. 260-271 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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title = "Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications",

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.",

keywords = "K-means algorithm, Trajectory planning, Unmanned aerial vehicles",

author = "Shuo Zhang and Xuemai Gu and Shuo Shi",

note = "Publisher Copyright: {\textcopyright} 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 2nd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2020 ; Conference date: 19-12-2020 Through 20-12-2020",

year = "2021",

doi = "10.1007/978-3-030-69066-3\_23",

language = "英语",

isbn = "9783030690656",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

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Zhang, S, Gu, X & Shi, S 2021, Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications. in S Shi, L Ye & Y Zhang (eds), Artificial Intelligence for Communications and Networks - 2nd EAI International Conference, AICON 2020, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 356 LNICST, Springer Science and Business Media Deutschland GmbH, pp. 260-271, 2nd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2020, Harbin, China, 19/12/20. https://doi.org/10.1007/978-3-030-69066-3_23

Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications. / Zhang, Shuo; Gu, Xuemai; Shi, Shuo.
Artificial Intelligence for Communications and Networks - 2nd EAI International Conference, AICON 2020, Proceedings. ed. / Shuo Shi; Liang Ye; Yu Zhang. Springer Science and Business Media Deutschland GmbH, 2021. p. 260-271 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 356 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications

AU - Zhang, Shuo

AU - Gu, Xuemai

AU - Shi, Shuo

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - K-means algorithm

KW - Trajectory planning

KW - Unmanned aerial vehicles

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U2 - 10.1007/978-3-030-69066-3_23

DO - 10.1007/978-3-030-69066-3_23

M3 - 会议稿件

AN - SCOPUS:85104412995

SN - 9783030690656

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 260

EP - 271

BT - Artificial Intelligence for Communications and Networks - 2nd EAI International Conference, AICON 2020, Proceedings

A2 - Shi, Shuo

A2 - Ye, Liang

A2 - Zhang, Yu

PB - Springer Science and Business Media Deutschland GmbH

T2 - 2nd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2020

Y2 - 19 December 2020 through 20 December 2020

ER -

Zhang S, Gu X, Shi S. Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications. In Shi S, Ye L, Zhang Y, editors, Artificial Intelligence for Communications and Networks - 2nd EAI International Conference, AICON 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 260-271. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-69066-3_23

Trajectory Planning Based on K-Means in UAV-Assisted Networks with Underlaid D2D Communications

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