Quantum Reinforcement Learning for Lightweight LEO Satellite Routing

Low Earth orbit (LEO) satellite networks have emerged as a promising solution, offering advantages, such as lower propagation delay, broader coverage, and rapid deployment capabilities. However, the dynamic topology and frequent handovers inherent in LEO satellite systems, coupled with limited onboa...

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Veröffentlicht in:	IEEE internet of things journal Jg. 12; H. 14; S. 28986 - 29004
Hauptverfasser:	Seon Kim, Gyu, Lee, Sungjoon, Cho, In-Sop, Park, Soohyun, Kim, Joongheon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 15.07.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Coordinates Deep learning Delay time Earth Handover Heuristic algorithms Intersatellite communications LEO satellite Lightweight low Earth orbit (LEO) satellite routing algorithm Low earth orbit satellites Low earth orbits Onboard equipment Parameters Planetary orbits quantum reinforcement learning (QRL) Rocket launches Routing Satellite networks Satellites Space vehicles Topology Training
ISSN:	2327-4662, 2327-4662
Online-Zugang:	Volltext
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