Volume 11, Number 23, December 2021

Modeling a 3G Mobile Phone base Radio using Artificial Intelligence Techniques

  Authors

Eduardo Calo^{1, 5}, Gabriel Vaca¹, Cristina Sánchez², David Jines³, Giovanny Amancha³, Ángel Flores³, Alex Santana G³ and Fernanda Oñate⁴, ¹Carrera Electricidad, Instituto Superior María Natalia Vaca, Ecuador, ²Carrera Mecánica Automotriz, Instituto Superior María Natalia Vaca, Ecuador, ³Carrera Electrónica, Instituto Superior María Natalia Vaca, Ecuador, ⁴Instituto Superior Tecnológico Pelileo-Baños, Ecuador, ⁵UNIR, Spain

  Abstract

The main objective of this work is to be able to use artificial intelligence techniques to be able to design a predictive model of the performance of a third-generation mobile phone base radio, using the analysis of KPIs obtained in a statistical data set of the daily behaviour of an RBS. For the realization of these models, various techniques such as Decision Trees, Neural Networks and Random Forest were used. which will allow faster progress in the deep analysis of large amounts of data statistics and get better results. In this part of the work, data was obtained from the behaviour of a third-party mobile phone base radio generation of the Claro operator in Ecuador, it should be noted that. The data are KPIs of the daily and hourly performance of a radio base of third generation mobile telephony, these data were obtained through the operator's remote monitoring and management tool Sure call PRS. To specify this practical case, several models were generated based on in various artificial intelligence technique for the prediction of performance results of a mobile phone base radio of third generation, the same ones that after several tests were creation of a predictive model that determines the performance of a mobile phone base radio. As a conclusion of this work, it was determined that the development of a predictive model based on artificial intelligence techniques is very useful for the analysis of large amounts of data in order to find or predict complex results, more quickly and trustworthy.

  Keywords

Neural Networks, Performance, Radio Base, Random Forest, Throughput.