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  Published Paper Details:

  Paper Title

MACHINE LEARNING TECHNIQUES FOR 5G AND BEYOND NETWORK CLASSIFICATION

  Authors

  K.TULASI KRISHNA KUMAR,  PURIJALA.KRISHNA PRIYA

  Keywords

Wireless Communication System, Artificial Intelligence, Machine Learning, Sixth Generation, Health Systems

  Abstract


Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. Here we try to use several ML algorithms to identify the suitable aspects related to the deployment of 5g and above networks by taking several features and ml algorithms into account. By comparing several algorithms we finally conclude which algorithm is best among several algorithms.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2210128

  Paper ID - 226254

  Page Number(s) - b65-b75

  Pubished in - Volume 10 | Issue 10 | October 2022

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  K.TULASI KRISHNA KUMAR,  PURIJALA.KRISHNA PRIYA,   "MACHINE LEARNING TECHNIQUES FOR 5G AND BEYOND NETWORK CLASSIFICATION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 10, pp.b65-b75, October 2022, Available at :http://www.ijcrt.org/papers/IJCRT2210128.pdf

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ISSN: 2320-2882
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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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