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| 1 | Cybertwin-Assisted Mode Selection in Ultra-Dense LEO Integrated Satellite-Terrestrial Network显示文摘Ultra-dense low earth orbit(LEO)integrated satellite-terrestrial network(ULISTN)has become an emerging paradigm to support massive access of Internet of things(IoT)in beyond fifth generation mobile networks(B5G).In ULISTN,there are two communication modes:cellular mode and satellite mode,where IoT users assessing terrestrial small base stations(TSBSs)and terrestrial-satellite terminals(TSTs)respectively.However,how to optimize the network performance and guarantee self-interests of the operator and IoT users in ULISTN is a challenging issue.In this paper,we propose a cybertwin-assisted joint mode selection and dynamic pricing(JMSDP)scheme for effective network management in ULISTN,where cybertwin serves as the intelligent agent.In JMSDP,the operator determines optimal access prices of TSBSs and TSTs,while each user selects the access mode according to access prices.Specifically,the operator conducts the Stackelberg game aiming at maximizing average throughput depending on the mode selection results of IoT users.Meanwhile,IoT users as followers adopt the evolutionary game to choose an access mode based on the access prices provided by the operator.Simulation results show that the proposed JMSDP can improve the average throughput and reduce the delay effectively,comparing with random access(RA)and maximum rate access. | Xin Zhang Bo Qian Xiaohan Qin Ting Ma Jiachen Chen Haibo Zhou Xuemin(Sherman)Shen | 2022 | Journal of Communications and Information Networks2022，7，4： | 1 |
| 2 | 面向B5G多业务场景基于D3QN的双时间尺度网络切片算法显示文摘为了有效满足不同切片的差异化服务质量需求,面向B5G多业务场景提出了一种基于竞争双深度Q网络(D3QN)的双时间尺度网络切片算法。研究了联合资源切片和调度问题,以归一化处理后的频谱效率和不同切片用户服务质量指数的加权和作为优化目标。在大时间尺度内,SDN控制器根据每种业务的资源需求利用D3QN算法预先分配给不同的切片,然后根据基站负载状态执行基站级资源更新。在小时间尺度内,基站通过轮询调度算法将资源调度到终端用户。仿真结果表明,所提算法在保证切片用户服务质量需求、频谱效率和系统效用方面具有优异的性能。与其他4种基准算法相比,所提算法的系统效用分别提升了3.22%、3.81%、7.48%和21.14%。 | 陈赓 齐书虎 沈斐 曾庆田 | 2022 | 通信学报2022，43，11： | 1 |
| 3 | Digital Twin-Empowered Network Planning for Multi-Tier Computing显示文摘In this paper,we design a resource management scheme to support stateful applications,which will be prevalent in sixth generation(6G)networks.Different from stateless applications,stateful applications require context data while executing computing tasks from user terminals(UTs).Using a multi-tier computing paradigm with servers deployed at the core network,gateways,and base stations to support stateful applications,we aim to optimize long-term resource reservation by jointly minimizing the usage of computing,storage,and communication resources and the cost of reconfiguring resource reservation.The coupling among different resources and the impact of UT mobility create challenges in resource management.To address the challenges,we develop digital twin(DT)empowered network planning with two elements,i.e.,multi-resource reservation and resource reservation reconfiguration.First,DTs are designed for collecting UT status data,based on which UTs are grouped according to their mobility patterns.Second,an algorithm is proposed to customize resource reservation for different groups to satisfy their different resource demands.Last,a Meta-learning-based approach is developed to reconfigure resource reservation for balancing the network resource usage and the reconfiguration cost.Simulation results demonstrate that the proposed DTempowered network planning outperforms benchmark frameworks by using less resources and incurring lower reconfiguration costs. | Conghao Zhou Jie Gao Mushu Li Xuemin(Sherman)Shen Weihua Zhuang | 2022 | Journal of Communications and Information Networks2022，7，3： | 0 |
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