Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks
This addresses connectivity challenges in non-terrestrial networks for beyond 5G systems, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the problem of optimizing packet forwarding between ground terminals via LEO satellites and UAVs to maximize data rate, achieving up to 5.74x higher average data rate compared to a baseline without satellite and UAV assistance.
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.