Big data analytics for dynamic network slicing in 5G and beyond with dynamic user preferences

Network slices represent a valuable technique to utilize the full resources of fifth-generation (5G) platforms. This allows verticals to control and utilize separate virtual systems on the above identical physical framework. In this article, we suggest a self-sustaining network slices (SNS) structur...

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Bibliographic Details
Published in:Optical and quantum electronics 2024, Vol.56 (1), Article 61
Main Authors: Syamala, Maganti, Anusuya, R., Sonkar, Sanjay Kumar, Goswami, Chandrashekhar, salunkhe, Satish, Elangovan, Muniyandy
Format: Article
Language:English
Subjects:
Characterization and Evaluation of Materials
Computer Communication Networks
Control systems
Electrical Engineering
Lasers
Learning
Network slicing
Optical Devices
Optics
Photonics
Physics
Physics and Astronomy
Quality of service architectures
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Summary:Network slices represent a valuable technique to utilize the full resources of fifth-generation (5G) platforms. This allows verticals to control and utilize separate virtual systems on the above identical physical framework. In this article, we suggest a self-sustaining network slices (SNS) structure that combines self-learning, self-slicing control efficiency optimization, and self-management regarding system facilities for achieving an adaptable control approach under unanticipated system circumstances. The forthcoming version of NodeB (gNodeB) layer splitting and packet schedule layer slicing, while networking level slices are the three layers that the suggested SNS paradigm decays the SNS command into such levels. At the networking stage, every gNodeB has access to network services over a long period with a coarse resolution of capabilities. Additionally, we employ a transfer-learning strategy to switch from a model-driven system to an autonomous, self-improving SNS monitoring. The suggested SNS architecture is intended to improve new applications' QoS efficiency significantly.
ISSN:0306-8919
1572-817X
DOI:10.1007/s11082-023-05663-2