Scheduling in Quantum Satellite Networks: Fairness and Performance Optimization

2025-12-07

AI Summary

This work addresses the dynamic satellite–ground station scheduling problem in quantum satellite networks under multiple physical and operational constraints. Method: We propose a multi-objective integer linear programming (ILP) framework that jointly models orbital dynamics, atmospheric attenuation, weather-induced disturbances, background noise, and inter-satellite links to capture realistic quantum communication impairments. The formulation simultaneously optimizes entanglement distribution throughput and inter-ground-station service fairness under resource constraints, entanglement fidelity thresholds, and fairness requirements, while supporting multi-satellite relaying. Contribution/Results: Experimental evaluation demonstrates that the framework quantifies the inherent throughput–fairness trade-off and significantly improves global scheduling efficiency. It constitutes the first scalable, reproducible, and physically realistic scheduling benchmark tailored for large-scale quantum satellite constellations.

Technology Category

• Planning, Routing, and Scheduling: Scheduling
• Constraint Satisfaction and Optimization: Satisfiability
• Machine Learning: Quantum Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Quantum satellite networks offer a promising solution for achieving long-distance quantum communication by enabling entanglement distribution across global scales. This work formulates and solves the quantum satellite network scheduling problem by optimizing satellite-to-ground station pair assignments under realistic system and environmental constraints. Our framework accounts for limited satellite and ground station resources, fairness, entanglement fidelity thresholds, and real world non-idealities including atmospheric losses, weather and background noise. In addition, we incorporate the complexities of multi-satellite relays enabled via inter-satellite links. We propose an integer linear programming (ILP) based optimization framework that supports multiple scheduling objectives, allowing us to analyze tradeoffs between maximizing total entanglement distribution rate and ensuring fairness across ground station pairs. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies.

Problem

• Optimizes satellite-ground station assignments for quantum entanglement distribution.
• Addresses resource constraints, fairness, and real-world environmental factors.
• Analyzes trade-offs between maximizing distribution rate and ensuring fairness.

Innovation

• Optimizes satellite-ground station assignments via integer linear programming
• Incorporates realistic constraints like atmospheric losses and fairness
• Supports multi-objective scheduling for rate-fairness tradeoff analysis