Adaptivemulti-layer deployment for a digital-twinempowered satellite-terrestrial integrated network  

作　　者：Yihong TAO Bo LEI Haoyang SHI Jingkai CHEN Xing ZHANG 

机构地区：[1]Wireless Signal Processing and Network Laboratory,Beijing University of Posts and Telecommunications,Beijing 100876,China [2]Research Institute of China Telecom Co.,Ltd.,Beijing 102209,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2025年第2期246-259,共14页信息与电子工程前沿(英文版)

基　　金：supported by the National Natural Science Foundation of China(No.62271062)。

摘　　要：With the development of satellite communication technology,satellite-terrestrial integrated networks(STINs),which integrate satellite networks and ground networks,can realize global seamless coverage of communication services.Confronting the intricacies of network dynamics,the 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 networks.Nevertheless,in the process of constructing a 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 a multi-layer DT deployment problem in the 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.

关 键 词：Digital twin Satellite-terrestrial integrated network DEPLOYMENT Multi-agent reinforcement learning 

分 类 号：TN91[电子电信—通信与信息系统]