Implanting Intelligence in 5G Mobile Networks—A Practical Approach

Sumbal Malik, Manzoor Ahmed Khan, Aadam, Hesham El-Sayed, Jalal Khan, Obaid Ullah

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

With the advancement in various technological fronts, we are expecting the design goals of smart cities to be realized earlier than expected. Undoubtedly, communication networks play the crucial role of backbone to all the verticals of smart cities, which is why we are surrounded by terminologies such as the Internet of Things, the Internet of Vehicles, the Internet of Medical Things, etc. In this paper, we focus on implanting intelligence in 5G and beyond mobile networks. In this connection, we design and develop a novel data-driven predictive model which may serve as an intelligent slicing framework for different verticals of smart cities. The proposed model is trained on different machine learning algorithms to predict the optimal network slice for a requested service resultantly assisting in allocating enough resources to the slice based on the traffic prediction.

Original languageEnglish
Article number3933
JournalElectronics (Switzerland)
Volume11
Issue number23
DOIs
Publication statusPublished - Dec 2022

Keywords

  • 5G
  • machine learning
  • network slicing
  • neural network
  • random forest

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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