General statistical analysis of cellular network parameter measurements in Quito
DOI:
https://doi.org/10.36825/RITI.10.21.011Abstract
The amount of data generated by the connection between a mobile terminal and the cellular network is high, and analyzing their behavior is a subject of research for several authors. But before starting an analysis, it is essential to perform a statistical analysis of the dataset, which should have been previously preprocessed, to know the behavior of the variables that make up the database. This article presents the statistical analysis of the measurements of the parameters of the cellular network in Quito city, collected from cellular applications. First, the data and analysis tools are described, in this case, Python and some libraries, then some statistical analyses are developed using graphs, which facilitate their interpretation. As result, we identified the variables that define the behavior of the data and whether they present an adequate distribution to obtain quality information. In addition, the correlation of the significant variables with the other variables was determined.
References
Bahra, N., Pierre, S. (2021). A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks. Telecom, 2 (2), 199-212. https://doi.org/10.3390/telecom2020013
Mei, L., Gou, J., Cai, Y., Cao, H., Liu, Y. (2021). Realtime Mobile Bandwidth and Handoff Predictions in 4G/5G Networks. http://arxiv.org/abs/2104.12959
Yajnanarayana, V., Rydén, H., Hévizi, L. (2020). 5G Handover using Reinforcement Learning. IEEE 3rd 5G World Forum (5GWF), Bangalore, India. https://doi.org/10.1109/5GWF49715.2020.9221072
Zhao, S., Jiang, X., Jacobson, G., Jana, R., Hsu, W. L., Rustamov, R., Talasila, M., Aftab, S. A., Chen, Y., Borcea, C. (2020). Cellular Network Traffic Prediction Incorporating Handover: A Graph Convolutional Approach. 17th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Como, Italy. https://doi.org/10.1109/SECON48991.2020.9158437
Abdah, H., Barraca, J. P., Aguiar, R. L. (2020). Handover Prediction Integrated with Service Migration in 5G Systems. IEEE International Conference on Communications (ICC), Dublin, Ireland. https://doi.org/10.1109/ICC40277.2020.9149426
Markopoulos, A., Pissaris, P., Kyriazakos, S., Dimitriadis, C., Sykas, E. D. (2002). Combining Position Location Information and Network Performance Data for Simulating Location Aided Handover. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.12.30&rep=rep1&type=pdf
Polese, M., Jana, R., Kounev, V., Zhang, K., Deb, S., Zorzi, M. (2021). IEEE Transactions on Mobile Computing, 20 (12), 3367-3382. https://doi.org/10.1109/TMC.2020.2999852
Mollel, M. S., Abubakar, A. I., Ozturk, M., Kaijage, S. F., Kisangiri, M., Hussain, S., Imran, M. A., Abbasi, Q. H. (2021). A Survey of Machine Learning Applications to Handover Management in 5G and Beyond. IEEE Access, 9, 45770-45802. https://doi.org/10.1109/ACCESS.2021.3067503
León Pirela, A. R., Pérez, C. E. (2019). Análisis estadístico en investigaciones positivistas: Medidas de tendencia central. Orbis: Revista Científica Electrónica de Ciencias Humanas, (43), 71-81. https://dialnet.unirioja.es/servlet/articulo?codigo=7065797
Weinberg, S. L., Harel, D., Abramowitz, S. K. (2021). Statistics Using R. Cambridge University Press.
Vázquez Carmona, E. V., Vázquez López, R., Herrera Lozada, J. C. (2019). Representación Gráfica de Datos Estadísticos en Python Utilizando la Biblioteca Matplotlib. https://www.boletin.upiita.ipn.mx/index.php/ciencia/845-cyt-numero-75/1775-representacion-grafica-de-datos-estadisticos-en-python-utilizando-la-biblioteca-matplotlib
Matplotlib. (2022). Matplotlib—Visualization with Python. https://matplotlib.org/
Ramos López, B. (2020). Librerías de Python para la visualización de datos. https://www.cursosgis.com/librerias-de-python-para-la-visualizacion-de-datos/
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