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.
Lee Ju-Hyung, Park Jihong, Bennis Mehdi, Ko Young-Chai
A4 Article in conference proceedings
Place of publication:
GLOBECOM 2020 – 2020 IEEE Global Communications Conference
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
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