# Integrated Scheduling Algorithm Based on Matching Game Theory in LEO Satellite Networks

## TL;DR

This paper introduces a new scheduling algorithm for LEO satellite networks to reduce communication delays using matching game theory.

## Contribution

The novel contribution is an integrated scheduling algorithm based on cyclic three-sided matching game theory for LEO satellite networks.

## Key findings

- The algorithm reduces average delay by 16.6% compared to existing methods.
- A theoretical model was developed to quantify the impact of scheduling on deterministic communication.
- The proposed algorithm effectively balances wired and wireless time slot allocation.

## Abstract

As an indispensable component of space–terrestrial integrated networks, low-Earth orbit (LEO) satellite networks are capable of providing flexible access and low delay communication services for emerging time-sensitive traffic. However, the inconsistent transmission rates between intra-satellite wired links and inter-satellite wireless links will undoubtedly result in unstable delay at the satellites. This disparity poses a challenge to ensuring deterministic communication for time-sensitive traffic. Aiming at this problem, we put forward an integrated scheduling algorithm based on matching game theory to concurrently determine the positions of wired and wireless time slots. First, we establish a theoretical model to quantify the influence of integrated scheduling on deterministic communication by elucidating the interrelationships among time-sensitive traffic, wired time slots, and wireless time slots. Second, drawing inspiration from scheduling sequences and matching game theory, the established integrated scheduling model is reformulated into a cyclic three-sided matching game model. Third, we design an integrated scheduling algorithm (ISA) to derive scheduling optimization solutions. Experimental results demonstrate that the proposed algorithm ISA outperforms existing scheduling algorithms, achieving an average delay reduction of 16.6% over all comparison algorithms.