Abstract
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.
Original language | English |
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Title of host publication | 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728182988 |
DOIs | |
Publication status | Published - 2020 Dec |
Event | 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China Duration: 2020 Dec 7 → 2020 Dec 11 |
Publication series
Name | 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings |
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Volume | 2020-January |
Conference
Conference | 2020 IEEE Global Communications Conference, GLOBECOM 2020 |
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Country/Territory | Taiwan, Province of China |
City | Virtual, Taipei |
Period | 20/12/7 → 20/12/11 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
ASJC Scopus subject areas
- Media Technology
- Modelling and Simulation
- Instrumentation
- Artificial Intelligence
- Computer Networks and Communications
- Hardware and Architecture
- Software
- Safety, Risk, Reliability and Quality