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Experimental coverage prediction in an LTE network using machine learning algorithms

Jorge Enrique Herrera Rubio
Universidad de Pamplona
German Arley Portilla González
Universidad de Pamplona
Ana María Osorio Velazco
Universidad de Pamplona
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Abstract

Objective: Experimentally predict the coverage of a Long Term Evolution (LTE) network using machine learning algorithms. Methodology: The research is descriptive through the analysis of the causes and factors that influence the variables, according to the quasi-experimental method through power measurements under controlled field conditions. The study of the physical variables that determine the coverage in the provision of a mobile network service is carried out by tracing four routes; the data is evaluated, pre-processed and filtered to test it using classical mathematical models, then statistical prediction methods are applied, as well as prediction algorithms with the help of machine learning (ML) to generate models that categorize the use of each cell within the grouping, among the methods to use are: descriptive statistics, Random Forest (RD), density quantification, Isolation Forest (IF) and Multivariable Outliers (MO). Subsequently, cross-validation and prediction of cell usage are performed. Results: With the values obtained, the correlation between the behavior of the power and distance parameters is established, and with the optimal coverage variables it is compared with the traditional models for a studied cell. Conclusions: The process of integrating mathematical and computational techniques facilitates the decision making of radio engineers in the diagnosis, management, planning and resizing of LTE networks under conditions where networks become much more heterogeneous and mobile services require of more technological infrastructure resources.

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How to Cite
[1]
J. E. Herrera Rubio, G. A. Portilla González, and A. M. Osorio Velazco, “Experimental coverage prediction in an LTE network using machine learning algorithms”, Investigación e Innovación en Ingenierías, vol. 12, no. 2, Nov. 2024.

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