Discrete quantum particle swarm optimization for beam hopping resource allocation in LEO satellite networks
Abstract
The exponential expansion of LEO constellations demands dynamic resource management to address non-uniform traffic distributions. This paper investigates the beam hopping resource scheduling problem for Low Earth Orbit (LEO) satellite constellations characterized by non-uniform traffic distributions. An integer programming-based optimization model is constructed, employing Jain’s Fairness Index to mitigate the excessive concentration of resources on high-demand cells, thereby striking a balance between system capacity and service timeliness. To optimize the spatiotemporal beam layout, a meta-heuristic strategy based on Discrete Quantum Particle Swarm Optimization (DQPSO) is proposed, incorporating a quantum potential energy update mechanism. Validated through comparative analysis against benchmark algorithms, the results demonstrate the significant superiority of DQPSO in maintaining low latency and ensuring high fairness.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.