Adaptive Multi-Layer Deployment for A Digital Twin Empowered Satellite-Terrestrial Integrated Network

TL;DR

This paper introduces a multi-layer digital twin deployment strategy in satellite-terrestrial networks, leveraging multi-agent reinforcement learning to optimize deployment and significantly reduce system delay.

Contribution

It proposes a novel multi-layer DT deployment framework in STINs and employs MARL to optimize deployment strategies, addressing flexibility and delay challenges.

Findings

Reduced system delay through multi-layer DT deployment

Effective MARL-based strategy for deployment optimization

Simulation results demonstrate performance improvements

Abstract

With the development of satellite communication technology, satellite-terrestrial integrated networks (STIN), which integrate satellite networks and ground networks, can realize seamless global coverage of communication services. Confronting the intricacies of network dynamics, the diversity of resource heterogeneity, and the unpredictability of user mobility, dynamic resource allocation within networks faces formidable challenges. Digital twin (DT), as a new technique, can reflect a physical network to a virtual network to monitor, analyze, and optimize the physical network. Nevertheless, in the process of constructing the DT model, the deployment location and resource allocation of DTs may adversely affect its performance. Therefore, we propose a STIN model, which alleviates the problem of insufficient single-layer deployment flexibility of the traditional edge network by deploying DTs in multi-layer nodes in a STIN. To address the challenge of deploying DTs in the network, we propose multi-layer DT deployment in a STIN to reduce system delay. Then we adopt a multi-agent reinforcement learning (MARL) scheme to explore the optimal strategy of the DT multi-layer deployment problem. The implemented scheme demonstrates a notable reduction in system delay, as evidenced by simulation outcomes.