Scheduling in Quantum Satellite Networks: Fairness and Performance Optimization

TL;DR

This paper develops an optimization framework for scheduling in quantum satellite networks, balancing entanglement distribution efficiency and fairness while considering real-world constraints and multi-satellite relays.

Contribution

It introduces an ILP-based scheduling method that accounts for resource limitations, environmental factors, and multi-satellite links, providing a comprehensive tool for performance analysis.

Findings

Supports multiple scheduling objectives and tradeoff analysis

Accounts for atmospheric losses, weather, and background noise

Serves as a benchmark for other scheduling policies

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.