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 onboard computational resources, necessitate the development of efficient and lightweight routing algorithms. Therefore, this paper proposes quantum reinforcement learning-based satellite routing (QRL-SR) tailored for LEO satellite networks. The QRL-SR algorithm addresses three critical considerations: (i) adapting to the dynamic and time-varying environment of LEO satellite networks; (ii) 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 (iii) 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 inter-satellite links. Finally, experimental results demonstrate that the QRL-SR achieves routing performance comparable to or outperforms conventional algorithms while significantly reducing computational resources.