University of Oulu

J. -H. Lee, J. Park, M. Bennis and Y. -C. Ko, "Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks," GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, Taiwan, 2020, pp. 1-6, doi: 10.1109/GLOBECOM42002.2020.9348105

Integrating LEO satellite and UAV relaying via reinforcement learning for non-terrestrial networks

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Author: Lee, Ju-Hyung1; Park, Jihong2; Bennis, Mehdi3;
Organizations: 1Electrical and Computer Engineering, Korea University, Seoul, Korea
2School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
3Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-02-25


A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. Integrating this with burgeoning unmanned aerial vehicle (UAV) assisted non-terrestrial networks will be a disruptive solution for beyond 5G systems provisioning large-scale three-dimensional connectivity. In this article, we study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation and a mobile high-altitude platform (HAP) such as a fixed-wing UAV. To maximize the end-to-end data rate, the satellite association and HAP location should be optimized, which is challenging due to a huge number of orbiting satellites and the resulting time-varying network topology. We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique. Simulation results corroborate that our proposed method achieves up to 5.74x higher average data rate compared to a direct communication baseline without SAT and HAP.

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Series: IEEE Global Communications Conference
ISSN: 2334-0983
ISSN-E: 2576-6813
ISSN-L: 2334-0983
ISBN: 978-1-7281-8298-8
ISBN Print: 978-1-7281-8299-5
Article number: 9348105
DOI: 10.1109/GLOBECOM42002.2020.9348105
Host publication: GLOBECOM 2020 - 2020 IEEE Global Communications Conference
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)(NRF-2018R1A2B2007789), and the EU-CHISTERA Projects LeadingEdge and CONNECT.
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