AI-Assisted Network-Slicing Based Next-Generation Wireless Networks
The integration of communications with different scales, diverse radio access technologies, and various network resources renders next-generation wireless networks (NGWNs) highly heterogeneous and dynamic. Emerging use cases and applications, such as machine to machine communications, autonomous dri...
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| Published in | IEEE open journal of vehicular technology Vol. 1; pp. 45 - 66 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2644-1330 2644-1330 |
| DOI | 10.1109/OJVT.2020.2965100 |
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| Abstract | The integration of communications with different scales, diverse radio access technologies, and various network resources renders next-generation wireless networks (NGWNs) highly heterogeneous and dynamic. Emerging use cases and applications, such as machine to machine communications, autonomous driving, and factory automation, have stringent requirements in terms of reliability, latency, throughput, and so on. Such requirements pose new challenges to architecture design, network management, and resource orchestration in NGWNs. Starting from illustrating these challenges, this paper aims at providing a good understanding of the overall architecture of NGWNs and three specific research problems under this architecture. First, we introduce a network-slicing based architecture and explain why and where artificial intelligence (AI) should be incorporated into this architecture. Second, the motivation, research challenges, existing works, and potential future directions related to applying AI-based approaches in three research problems are described in detail, i.e., flexible radio access network slicing, automated radio access technology selection, and mobile edge caching and content delivery. In summary, this paper highlights the benefits and potentials of AI-based approaches in the research of NGWNs. |
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| AbstractList | The integration of communications with different scales, diverse radio access technologies, and various network resources renders next-generation wireless networks (NGWNs) highly heterogeneous and dynamic. Emerging use cases and applications, such as machine to machine communications, autonomous driving, and factory automation, have stringent requirements in terms of reliability, latency, throughput, and so on. Such requirements pose new challenges to architecture design, network management, and resource orchestration in NGWNs. Starting from illustrating these challenges, this paper aims at providing a good understanding of the overall architecture of NGWNs and three specific research problems under this architecture. First, we introduce a network-slicing based architecture and explain why and where artificial intelligence (AI) should be incorporated into this architecture. Second, the motivation, research challenges, existing works, and potential future directions related to applying AI-based approaches in three research problems are described in detail, i.e., flexible radio access network slicing, automated radio access technology selection, and mobile edge caching and content delivery. In summary, this paper highlights the benefits and potentials of AI-based approaches in the research of NGWNs. |
| Author | Lyu, Kangjia Li, Xu Shen, Xuemin Zhuang, Weihua Gao, Jie Li, Mushu Wu, Wen Rao, Jaya |
| Author_xml | – sequence: 1 givenname: Xuemin surname: Shen fullname: Shen, Xuemin email: sshen@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 2 givenname: Jie orcidid: 0000-0001-6095-2968 surname: Gao fullname: Gao, Jie email: jie.gao@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 3 givenname: Wen orcidid: 0000-0002-0458-1282 surname: Wu fullname: Wu, Wen email: wen.wu@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 4 givenname: Kangjia surname: Lyu fullname: Lyu, Kangjia email: kangjia.lyu@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 5 givenname: Mushu orcidid: 0000-0002-9694-3294 surname: Li fullname: Li, Mushu email: mushu.li@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 6 givenname: Weihua orcidid: 0000-0003-0488-511X surname: Zhuang fullname: Zhuang, Weihua email: wzhuang@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 7 givenname: Xu surname: Li fullname: Li, Xu email: xu.lica@huawei.com organization: Huawei Technologies Canada Inc., Ottawa, ON, Canada – sequence: 8 givenname: Jaya surname: Rao fullname: Rao, Jaya email: jaya.rao@huawei.com organization: Huawei Technologies Canada Inc., Ottawa, ON, Canada |
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| SubjectTerms | Artificial intelligence Computer architecture content placement and delivery heterogeneous networks machine learning Network slicing Next generation networking Next-generation wireless networks radio access network slicing radio access technology selection Resource management Vehicle dynamics Wireless networks |
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| Title | AI-Assisted Network-Slicing Based Next-Generation Wireless Networks |
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