The Experts below are selected from a list of 76179 Experts worldwide ranked by ideXlab platform
Kan Zheng - One of the best experts on this subject based on the ideXlab platform.
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intelligent network Slicing for V2X Services Toward 5G
IEEE Network, 2019Co-Authors: Jie Mei, Xianbin Wang, Kan ZhengAbstract:Benefiting from the widely deployed LTE infrastructures, 5G wireless networks are becoming a critical enabler for the emerging V2X communications. However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and the design of an ML algorithm, are discussed to tackle the related challenges. Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With the integration of 5G network slicing and ML technologies, the QoS of V2X services is expected to be dramatically enhanced.
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intelligent network Slicing for V2X Services Towards 5G.
arXiv: Networking and Internet Architecture, 2019Co-Authors: Jie Mei, Xianbin Wang, Kan ZhengAbstract:Benefiting from the widely deployed LTE infrastructures, the fifth generation (5G) wireless networks have been becoming a critical enabler for the emerging vehicle-to-everything (V2X) communications. However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and ML algorithm design, are discussed to tackle the related challenges. Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With integration of 5G network slicing and ML-enabled technologies, the QoS of V2X services is expected to be dramatically enhanced.
Parminder Mudhar - One of the best experts on this subject based on the ideXlab platform.
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IS&N - A Service Creation Environment for a Future intelligent network
Lecture Notes in Computer Science, 1994Co-Authors: Parminder MudharAbstract:This paper presents the model of the service creation environment (SCE) for a future intelligent network developed within EURESCOM project P103, “Evolution of the intelligent network”. The SCE models the service creation phase of the service lifecycle using an object oriented service composition technique developed within the project. The SCE model incorporates a model for service constituents storage and guidelines for service SCE interaction and security. Finally all concepts used to describe the SCE are defined to remove ambiguity in their use.
Jie Mei - One of the best experts on this subject based on the ideXlab platform.
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intelligent network Slicing for V2X Services Toward 5G
IEEE Network, 2019Co-Authors: Jie Mei, Xianbin Wang, Kan ZhengAbstract:Benefiting from the widely deployed LTE infrastructures, 5G wireless networks are becoming a critical enabler for the emerging V2X communications. However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and the design of an ML algorithm, are discussed to tackle the related challenges. Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With the integration of 5G network slicing and ML technologies, the QoS of V2X services is expected to be dramatically enhanced.
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intelligent network Slicing for V2X Services Towards 5G.
arXiv: Networking and Internet Architecture, 2019Co-Authors: Jie Mei, Xianbin Wang, Kan ZhengAbstract:Benefiting from the widely deployed LTE infrastructures, the fifth generation (5G) wireless networks have been becoming a critical enabler for the emerging vehicle-to-everything (V2X) communications. However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and ML algorithm design, are discussed to tackle the related challenges. Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With integration of 5G network slicing and ML-enabled technologies, the QoS of V2X services is expected to be dramatically enhanced.
Liang Cheng - One of the best experts on this subject based on the ideXlab platform.
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Wireless awareness for wireless intelligent network
9th Asia-Pacific Conference on Communications (IEEE Cat. No.03EX732), 2003Co-Authors: Liang ChengAbstract:Wireless awareness for wireless intelligent network is the capability of network services to be aware of the existence and characteristics of the wireless links in the communication path. This position paper summarizes several intrusive and nonintrusive wireless awareness techniques and proposes a new framework design for integrating wireless awareness into the wireless network to improve network intelligence for next-generation wireless communication networks.
Xianbin Wang - One of the best experts on this subject based on the ideXlab platform.
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intelligent network Slicing for V2X Services Toward 5G
IEEE Network, 2019Co-Authors: Jie Mei, Xianbin Wang, Kan ZhengAbstract:Benefiting from the widely deployed LTE infrastructures, 5G wireless networks are becoming a critical enabler for the emerging V2X communications. However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and the design of an ML algorithm, are discussed to tackle the related challenges. Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With the integration of 5G network slicing and ML technologies, the QoS of V2X services is expected to be dramatically enhanced.
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intelligent network Slicing for V2X Services Towards 5G.
arXiv: Networking and Internet Architecture, 2019Co-Authors: Jie Mei, Xianbin Wang, Kan ZhengAbstract:Benefiting from the widely deployed LTE infrastructures, the fifth generation (5G) wireless networks have been becoming a critical enabler for the emerging vehicle-to-everything (V2X) communications. However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and ML algorithm design, are discussed to tackle the related challenges. Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With integration of 5G network slicing and ML-enabled technologies, the QoS of V2X services is expected to be dramatically enhanced.
