JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

Adaptive multi-layer deployment for a digital twin-empowered satellite-terrestrial integrated network

Author(s): Yihong TAO, Bo LEI, Haoyang SHI, Jingkai CHEN, Xing ZHANG
Affiliation(s): Wireless Signal Processing and Network Laboratory Beijing University of Posts and Telecommunications, Beijing 100876, China; more
Corresponding email(s): hszhang@bupt.edu.cn
Key Words: Digital twin; Satellite-terrestrial integrated network; Deployment; Multi-agent reinforcement learning

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Yihong TAO, Bo LEI, Haoyang SHI, Jingkai CHEN, Xing ZHANG. Adaptive multi-layer deployment for a digital twin-empowered satellite-terrestrial integrated network[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400327

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author="Yihong TAO, Bo LEI, Haoyang SHI, Jingkai CHEN, Xing ZHANG",
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year="in press",
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doi="https://doi.org/10.1631/FITEE.2400327"
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%T Adaptive multi-layer deployment for a digital twin-empowered satellite-terrestrial integrated network
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%A Jingkai CHEN
%A Xing ZHANG
%J Frontiers of Information Technology & Electronic Engineering
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doi="https://doi.org/10.1631/FITEE.2400327"

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Abstract
Chinese Summary
Academic Network
Reviewer Comment

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.

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