Time Allocation and Trajectory Design in NOMA-based UAV-Enabled Radio Frequency Energy Harvesting Network

Yuchen Li; Shuo Shi; Chenyu Wu; Zhenyu Xu

doi:10.1109/VTC2023-Fall60731.2023.10333747

Time Allocation and Trajectory Design in NOMA-based UAV-Enabled Radio Frequency Energy Harvesting Network

Yuchen Li^*
, Shuo Shi
, Chenyu Wu
, Zhenyu Xu

^*Corresponding author for this work

School of Electronics and Information Engineering, Harbin Institute of Technology
Huizhou Engineering Vocational College

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

5 Scopus citations

Abstract

In order to solve the problem with energy supply, this paper studies an unmanned aerial vehicle (UAV)-enabled radio frequency energy harvesting (RFEH) system with non-orthogonal multiple access (NOMA). Time allocation and UAV's trajectory are jointly optimized to maximize the energy efficiency under a novel unevenly discretized trajectory design. Lagrange multiplier method is used to perform time allocation optimization, where the Dinkelbach algorithm, difference of convex (DC) programming, and successive convex approximation (SCA) are used to fix the non-convexity of the problem. In addition, we put forward a trajectory optimization method based on Q-learning. Time allocation and trajectory are then optimized alternately. Finally, simulation results show that our proposed design achieves better energy efficiency than benchmark schemes.

Original language	English
Title of host publication	2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350329285
DOIs	https://doi.org/10.1109/VTC2023-Fall60731.2023.10333747
State	Published - 2023
Externally published	Yes
Event	98th IEEE Vehicular Technology Conference, VTC 2023-Fall - Hong Kong, China Duration: 10 Oct 2023 → 13 Oct 2023

Publication series

Name	IEEE Vehicular Technology Conference
ISSN (Print)	1550-2252

Conference

Conference	98th IEEE Vehicular Technology Conference, VTC 2023-Fall
Country/Territory	China
City	Hong Kong
Period	10/10/23 → 13/10/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

UAV-enabled network
energy harvesting
time allocation
trajectory design

Access to Document

10.1109/VTC2023-Fall60731.2023.10333747

Cite this

Li, Y., Shi, S., Wu, C., & Xu, Z. (2023). Time Allocation and Trajectory Design in NOMA-based UAV-Enabled Radio Frequency Energy Harvesting Network. In 2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings (IEEE Vehicular Technology Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2023-Fall60731.2023.10333747

@inproceedings{4c170c03b5234c23a37e80912c1463af,

title = "Time Allocation and Trajectory Design in NOMA-based UAV-Enabled Radio Frequency Energy Harvesting Network",

abstract = "In order to solve the problem with energy supply, this paper studies an unmanned aerial vehicle (UAV)-enabled radio frequency energy harvesting (RFEH) system with non-orthogonal multiple access (NOMA). Time allocation and UAV's trajectory are jointly optimized to maximize the energy efficiency under a novel unevenly discretized trajectory design. Lagrange multiplier method is used to perform time allocation optimization, where the Dinkelbach algorithm, difference of convex (DC) programming, and successive convex approximation (SCA) are used to fix the non-convexity of the problem. In addition, we put forward a trajectory optimization method based on Q-learning. Time allocation and trajectory are then optimized alternately. Finally, simulation results show that our proposed design achieves better energy efficiency than benchmark schemes.",

keywords = "UAV-enabled network, energy harvesting, time allocation, trajectory design",

author = "Yuchen Li and Shuo Shi and Chenyu Wu and Zhenyu Xu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 98th IEEE Vehicular Technology Conference, VTC 2023-Fall ; Conference date: 10-10-2023 Through 13-10-2023",

year = "2023",

doi = "10.1109/VTC2023-Fall60731.2023.10333747",

language = "英语",

series = "IEEE Vehicular Technology Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings",

address = "美国",

}

Li, Y, Shi, S , Wu, C & Xu, Z 2023, Time Allocation and Trajectory Design in NOMA-based UAV-Enabled Radio Frequency Energy Harvesting Network. in 2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings. IEEE Vehicular Technology Conference, Institute of Electrical and Electronics Engineers Inc., 98th IEEE Vehicular Technology Conference, VTC 2023-Fall, Hong Kong, China, 10/10/23. https://doi.org/10.1109/VTC2023-Fall60731.2023.10333747

Time Allocation and Trajectory Design in NOMA-based UAV-Enabled Radio Frequency Energy Harvesting Network. / Li, Yuchen; Shi, Shuo ; Wu, Chenyu et al.
2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (IEEE Vehicular Technology Conference).

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

TY - GEN

T1 - Time Allocation and Trajectory Design in NOMA-based UAV-Enabled Radio Frequency Energy Harvesting Network

AU - Li, Yuchen

AU - Shi, Shuo

AU - Wu, Chenyu

AU - Xu, Zhenyu

PY - 2023

Y1 - 2023

N2 - In order to solve the problem with energy supply, this paper studies an unmanned aerial vehicle (UAV)-enabled radio frequency energy harvesting (RFEH) system with non-orthogonal multiple access (NOMA). Time allocation and UAV's trajectory are jointly optimized to maximize the energy efficiency under a novel unevenly discretized trajectory design. Lagrange multiplier method is used to perform time allocation optimization, where the Dinkelbach algorithm, difference of convex (DC) programming, and successive convex approximation (SCA) are used to fix the non-convexity of the problem. In addition, we put forward a trajectory optimization method based on Q-learning. Time allocation and trajectory are then optimized alternately. Finally, simulation results show that our proposed design achieves better energy efficiency than benchmark schemes.

AB - In order to solve the problem with energy supply, this paper studies an unmanned aerial vehicle (UAV)-enabled radio frequency energy harvesting (RFEH) system with non-orthogonal multiple access (NOMA). Time allocation and UAV's trajectory are jointly optimized to maximize the energy efficiency under a novel unevenly discretized trajectory design. Lagrange multiplier method is used to perform time allocation optimization, where the Dinkelbach algorithm, difference of convex (DC) programming, and successive convex approximation (SCA) are used to fix the non-convexity of the problem. In addition, we put forward a trajectory optimization method based on Q-learning. Time allocation and trajectory are then optimized alternately. Finally, simulation results show that our proposed design achieves better energy efficiency than benchmark schemes.

KW - UAV-enabled network

KW - energy harvesting

KW - time allocation

KW - trajectory design

UR - https://www.scopus.com/pages/publications/85181167670

U2 - 10.1109/VTC2023-Fall60731.2023.10333747

DO - 10.1109/VTC2023-Fall60731.2023.10333747

M3 - 会议稿件

AN - SCOPUS:85181167670

T3 - IEEE Vehicular Technology Conference

BT - 2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 98th IEEE Vehicular Technology Conference, VTC 2023-Fall

Y2 - 10 October 2023 through 13 October 2023

ER -

Time Allocation and Trajectory Design in NOMA-based UAV-Enabled Radio Frequency Energy Harvesting Network

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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