  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.
