About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 – December 1, 2019, Proceedings, Part II

Research Article

Gradient-Based UAV Positioning Algorithm for Throughput Optimization in UAV Relay Networks

Download(Requires a free EAI acccount)
6 downloads
Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-030-41117-6_23,
        author={Xiangyu Li and Tao Peng and Xiaoyang Li},
        title={Gradient-Based UAV Positioning Algorithm for Throughput Optimization in UAV Relay Networks},
        proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part II},
        proceedings_a={CHINACOM PART 2},
        year={2020},
        month={2},
        keywords={UAV relay network Throughput optimization Gradient-based positioning},
        doi={10.1007/978-3-030-41117-6_23}
    }
    
  • Xiangyu Li
    Tao Peng
    Xiaoyang Li
    Year: 2020
    Gradient-Based UAV Positioning Algorithm for Throughput Optimization in UAV Relay Networks
    CHINACOM PART 2
    Springer
    DOI: 10.1007/978-3-030-41117-6_23
Xiangyu Li1,*, Tao Peng1, Xiaoyang Li1
  • 1: Wireless Signal Processing and Networks Laboratory (WSPN), Key Laboratory of Universal Wireless Communications, Ministry of Education
*Contact email: lixymiracle@bupt.edu.cn

Abstract

Under natural disaster or other emergency situations, the fixed communication infrastructures are unavailable, which brings great inconvenience to information interaction among people. In this paper, we design a UAV relay network, using a small-scale UAV fleet serves as communication relays of a team of ground users performing collaborate tasks. Aiming at the user’s requirement for high communication capacity for multi service transmission, we present a distributed gradient-based algorithm of finding the optimal positions of UAV in UAV relay network to improve the network average end-to-end throughput in real-time. The system optimization objective is formulated by using Shannon-Hartley Theorem and received signal-to-noise ratio (SNR) that incorporates with UAV positions and ground user positions. Due to the non-smoothness of the objective function, we use generalized gradient instead. Each UAV moves along the generalized gradient direction of objective function to optimize the target locally, and finally, all UAV convergence to stable positions of optimizing the network throughput. Simulation results show the effectiveness of our method in improving the network average end-to-end throughput.

Keywords
UAV relay network Throughput optimization Gradient-based positioning
Published
2020-02-27
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-41117-6_23
Copyright © 2019–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL