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Adaptive multi-layer deployment for a digital twin-empowered satellite-terrestrial integrated network
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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, 1998, -1(-1): .
@article{title="Adaptive multi-layer deployment for a digital twin-empowered satellite-terrestrial integrated network",
author="Yihong TAO, Bo LEI, Haoyang SHI, Jingkai CHEN, Xing ZHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400327"
}
%0 Journal Article
%T Adaptive multi-layer deployment for a digital twin-empowered satellite-terrestrial integrated network
%A Yihong TAO
%A Bo LEI
%A Haoyang SHI
%A Jingkai CHEN
%A Xing ZHANG
%J Journal of Zhejiang University SCIENCE C
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%N -1
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%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400327
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T1 - Adaptive multi-layer deployment for a digital twin-empowered satellite-terrestrial integrated network
A1 - Yihong TAO
A1 - Bo LEI
A1 - Haoyang SHI
A1 - Jingkai CHEN
A1 - Xing ZHANG
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
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%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2400327
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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