Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks

Ju Hyung Lee, Jihong Park, Mehdi Bennis, Young Chai Ko

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Integrating low-altitude earth orbit (LEO) satellites (SATs) and unmanned aerial vehicles (UAVs) within a non-terrestrial network (NTN), we investigate the problem of forwarding packets between two faraway ground terminals through SAT and UAV relays using either radio-frequency (RF) or free-space optical (FSO) link. Towards maximizing the communication efficiency, the associations with orbiting SATs and the trajectories of UAVs should be optimized, which is challenging due to the time-varying network topology and a huge number of possible control actions. To overcome the difficulty, we lift this problem to multi-agent deep reinforcement learning with a novel action dimensionality reduction technique. Simulation results corroborate that our proposed SAT-UAV integrated scheme achieves 1.99x higher end-to-end sum throughput compared to a benchmark scheme with fixed ground relays. While improving the throughput, our proposed scheme also aims to reduce the UAV control energy, yielding 2.25x higher energy efficiency than a baseline method only maximizing the throughput. Lastly, thanks to utilizing hybrid FSO/RF links, the proposed scheme achieves up to 62.56x higher peak throughput and 21.09x higher worst-case throughput than the cases utilizing either RF or FSO links, highlighting the importance of co-designing SAT-UAV associations, UAV trajectories, and hybrid FSO/RF links in beyond-5 G NTNs.

Original languageEnglish
Pages (from-to)3647-3662
Number of pages16
JournalIEEE Transactions on Vehicular Technology
Issue number3
Publication statusPublished - 2023 Mar 1

Bibliographical note

Publisher Copyright:
© 1967-2012 IEEE.


  • LEO satellite
  • UAV
  • hybrid FSO/RF
  • multi-agent deep reinforcement learning
  • non-terrestrial network

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics


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