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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Bibliographic Details
Published in:IEEE internet of things journal Vol. 12; no. 14; pp. 28986 - 29004
Main Authors: Seon Kim, Gyu, Lee, Sungjoon, Cho, In-Sop, Park, Soohyun, Kim, Joongheon
Format: Journal Article
Language:English
Published: Piscataway IEEE 15.07.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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 onboard computational resources, necessitate the development of efficient and lightweight routing algorithms. Therefore, this article proposes quantum reinforcement learning-based satellite routing (QRL-SR) tailored for LEO satellite networks. The QRL-SR algorithm addresses three critical considerations: 1) adapting to the dynamic and time-varying environment of LEO satellite networks; 2) incorporating LEO satellite geometry by transforming celestial coordinate data, specifically two-line element, into orbital coordinate systems for accurate LEO satellite positioning over time; and 3) being designed to be lightweight by leveraging QRL to reduce the number of training parameters. The proposed QRL-SR efficiently trains routing policies with fewer parameters, aligning with LEO satellites' small-size, weight, and power (SWaP) constraints. The primary purpose of the QRL-SR-based LEO satellites is to reduce free space path loss, delay time, and the number of hops needed for routing through the intersatellite links. Finally, experimental results demonstrate that the QRL-SR achieves routing performance comparable to or outperforms conventional algorithms while significantly reducing computational resources.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3568454