Brief Survey: Machine Learning in Handover Cellular Network †

Viviana Párraga-Villamar; Pablo Lupera-Morillo; Felipe Grijalva; Henry Carvajal

doi:10.3390/engproc2023047002

Brief Survey: Machine Learning in Handover Cellular Network †

Viviana Párraga-Villamar^*
, Pablo Lupera-Morillo
, Felipe Grijalva
, Henry Carvajal

^*Autor correspondiente de este trabajo

Escuela Politecnica Nacional
Universidad de las Americas - Ecuador

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

5 Citas (Scopus)

Resumen

The proposed work offers a concise review of the application of machine learning (ML) to cellular network handovers (HO) via the Systematic Mapping Study (SMS) methodology, emphasizing the problem areas and requirements. The key points include the paramount role of high-quality data, with meticulous data acquisition and preprocessing as vital steps in ML dataset construction. The article identifies prevalent parameters for HO enhancement and underscores the diversity of ML algorithms, aligning them with specific data input and tasks. This study establishes a robust basis for forthcoming research in applying machine learning to cellular network HOs.

Idioma original	Inglés
Número de artículo	2
Publicación	Engineering Proceedings
Volumen	47
N.º	1
DOI	https://doi.org/10.3390/engproc2023047002
Estado	Publicada - 2023

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Researchers from Departamento de Electronica Detail New Studies and Findings in the Area of Machine Learning (Brief Survey: Machine Learning in Handover Cellular Network)
Grijalva Arévalo, F.
10/04/24
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abstract = "The proposed work offers a concise review of the application of machine learning (ML) to cellular network handovers (HO) via the Systematic Mapping Study (SMS) methodology, emphasizing the problem areas and requirements. The key points include the paramount role of high-quality data, with meticulous data acquisition and preprocessing as vital steps in ML dataset construction. The article identifies prevalent parameters for HO enhancement and underscores the diversity of ML algorithms, aligning them with specific data input and tasks. This study establishes a robust basis for forthcoming research in applying machine learning to cellular network HOs.",

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AU - Carvajal, Henry

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AB - The proposed work offers a concise review of the application of machine learning (ML) to cellular network handovers (HO) via the Systematic Mapping Study (SMS) methodology, emphasizing the problem areas and requirements. The key points include the paramount role of high-quality data, with meticulous data acquisition and preprocessing as vital steps in ML dataset construction. The article identifies prevalent parameters for HO enhancement and underscores the diversity of ML algorithms, aligning them with specific data input and tasks. This study establishes a robust basis for forthcoming research in applying machine learning to cellular network HOs.

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KW - handover

KW - machine learning

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JO - Engineering Proceedings

JF - Engineering Proceedings

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Brief Survey: Machine Learning in Handover Cellular Network †

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Researchers from Departamento de Electronica Detail New Studies and Findings in the Area of Machine Learning (Brief Survey: Machine Learning in Handover Cellular Network)

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