Digital Twin-Assisted Robust and Adaptive Resource Slicing in LEO Satellite Networks

TL;DR

This paper introduces a digital twin-assisted adaptive resource slicing scheme for low Earth orbit satellite networks, improving robustness and efficiency amid dynamic demands and satellite mobility.

Contribution

It proposes a novel digital twin framework that captures demand uncertainty and enables adaptive, robust resource slicing in LEO satellite networks.

Findings

Outperforms benchmark methods in reducing service demand violations

Achieves efficient resource utilization in dynamic scenarios

Enhances robustness against demand prediction errors

Abstract

Resource slicing in low Earth orbit satellite networks (LSN) is essential to support diversified services. In this paper, we investigate a resource slicing problem in LSN to reserve resources in satellites to achieve efficient resource provisioning. To address the challenges of non-stationary service demands, inaccurate prediction, and satellite mobility, we propose an adaptive digital twin (DT)-assisted resource slicing scheme for robust and adaptive resource management in LSN. Specifically, a slice DT, being able to capture the service demand prediction uncertainty through collected service demand data, is constructed to enhance the robustness of resource slicing decisions for dynamic service demands. In addition, the constructed DT can emulate resource slicing decisions for evaluating their performance, enabling adaptive slicing decision updates to efficiently reserve resources in LSN. Simulation results demonstrate that the proposed scheme outperforms benchmark methods, achieving low service demand violations with efficient resource consumption.