| Autor: |
Nan Li, Xiaofei Xu, Qi Sun, Jie Wu, Qiao Zhang, Gangyi Chi, Chih-Lin I, Nurit Sprecher |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 11, Pp 4443-4454 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3234493 |
| Popis: |
Intelligence and cloudification are widely recognized as key driving forces in the evolution of 5G radio access network (RAN). This paper presents a promising architecture framework for the evolution of 5G radio access network, enabled by a deep integration with cloudification and artificial intelligence/machine learning (AI/ML) technologies. To accommodate the diversified scenarios and services and handle the complexity of the 5G network in a flexible and efficient manner, the architecture framework highlights three concepts: convergence of RAN and cloud, RAN empowered by hierarchical AI capabilities, and mutual awareness between RAN and services. The key design aspects and technologies that realize those concepts are discussed systematically. Two typical use cases including the RAN slice resource allocation optimization and RAN-aware video service assurance, are demonstrated along with the simulation or lab test results to validate the potential of the architecture framework. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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