TY - GEN
T1 - Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks
AU - Lee, Ju Hyung
AU - Park, Jihong
AU - Bennis, Mehdi
AU - Ko, Young Chai
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85101243115&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM42002.2020.9348105
DO - 10.1109/GLOBECOM42002.2020.9348105
M3 - Conference contribution
AN - SCOPUS:85101243115
T3 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
BT - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Global Communications Conference, GLOBECOM 2020
Y2 - 7 December 2020 through 11 December 2020
ER -