Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks

• URL: http://arxiv.org/abs/2005.12521v1
• Date: Tue, 26 May 2020 05:39:27 GMT
• Title: Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks
• Authors: Ju-Hyung Lee, Jihong Park, Mehdi Bennis, and Young-Chai Ko
• Abstract summary: A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation. To maximize the end-to-end data rate, the satellite association and HAP location should be optimized. We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
• Score: 51.05735925326235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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