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

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Abstract

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.

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Funding

This study did not receive any funding in any form.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist., Andhra Pradesh, 522502, India

    Maganti Syamala

  2. Department of Computer Science and Engineering, Modern Institute of Technology and Research Center, Alwar, Rajasthan, India

    R. Anusuya

  3. Department of Computer Science & Information Systems, Shri Ramswaroop Memorial University, Lucknow-Deva Road, Barabanki (UP), India

    Sanjay Kumar Sonkar

  4. Department of CSE, School of Computing, MIT ADT University, Pune, Maharashtra, 412201, India

    Chandrashekhar Goswami

  5. Computer Engineering Department, Terna Engineering College, Navi Mumbai, Affiliated to University of Mumbai, India

    Satish salunkhe

  6. Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India

    Muniyandy Elangovan

  7. Department of R&D, Bond Marine Consultancy, London, EC1V 2NX, UK

    Muniyandy Elangovan

Authors
  1. Maganti Syamala
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  2. R. Anusuya
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  3. Sanjay Kumar Sonkar
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  4. Chandrashekhar Goswami
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  5. Satish salunkhe
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  6. Muniyandy Elangovan
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Contributions

MS and Dr. RA conceptualized and designed the research project, with Dr. A providing supervision and guidance throughout the study. SKS conducted data collection, analysis, and played a crucial role in the technical implementation. Dr. CGcontributed to the theoretical framework and provided valuable insights into network slicing. SS was responsible for the experimental setup and data acquisition. Dr. ME participated in the data analysis and interpretation. All authors collectively contributed to the manuscript writing, revision, and finalization, ensuring the quality and accuracy of the research findings presented in the paper.

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Correspondence to Maganti Syamala.

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Syamala, M., Anusuya, R., Sonkar, S.K. et al. Big data analytics for dynamic network slicing in 5G and beyond with dynamic user preferences. Opt Quant Electron 56, 61 (2024). https://doi.org/10.1007/s11082-023-05663-2

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  • DOI: https://doi.org/10.1007/s11082-023-05663-2

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