Uncovering mobile network infrastructure in Mexico using crowdsourced data
PDF (Español (España))
XML (Español (España))

Keywords

deployment strategies
Mobile Network Operators (MNO)
LTE
crowdsourced data
communication
telecommunications
Internet
net
technology
mobile infrastructure
mobile coverage
connectivity
network capacity
mobile communications estrategias de despliegue
Operadores Móviles en Red (OMR)
LTE
datos colaborativos
comunicación
telecomunicaciones
Internet
red
tecnología
infraestructura móvil
cobertura móvil
conectividad
capacidad de la red
comunicaciones móviles

How to Cite

Contreras Potenciano, L. I., Ovando Chico, C., & Frías, Z. (2022). Uncovering mobile network infrastructure in Mexico using crowdsourced data. Nova Scientia, 14(28). https://doi.org/10.21640/ns.v14i28.2954

Abstract

It is known that access to mobile telecommunications, and through them to the Internet, can provide greater social well-being. Likewise, within the telecommunications sector there was a growing demand in the use and access to services. This generates the need for mobile operators to expand and increase the capacity of their telecommunications networks. In order to achieve this, it is important to analyze the strategies for increasing the capacity and coverage of the mobile infrastructure of the main mobile operators. A longitudinal analysis of the node density by geographic area was carried out at the municipal level. Likewise, the four main mobile operators with a network in Mexico were analyzed using crowdsourced data. Also, the data was filtered by LTE technology and a correspondence was made between the nodes and the frequency bands used by each operator. The operators' network capacity increase strategies and the percentage of use of the frequency bands assigned to each operator were analyzed. As a result, the main mobile network operators in Mexico have followed a similar coverage strategy. Likewise, in the analyzed data, a positive correlation was not found between node densification and the technological characteristics of the frequency bands. It is suggested to invest in new infrastructure for the deployment of new technologies, and to promote mobile coverage in rural areas. Finally, it is suggested to facilitate the leasing of bands on various frequencies that allow operators to take advantage of their technological characteristics; through costs that are aligned with the international market.

https://doi.org/10.21640/ns.v14i28.2954
PDF (Español (España))
XML (Español (España))

References

Altan Redes. (2020). Altán Redes: desarrollador de la Red Compartida Agenda. http://www.ift.org.mx/sites/default/files/presentacion_isabel_prieto_altan-panel_ift.pdf

Altan Redes. (2021). Our coverage. Altan Redes. https://www.altanredes.com/en/solutions-to-operators/our-coverage/

Álvarez, C. L. (2018). Telecomunicaciones y Radiodifusión en México. Santi Ediciones.

Bnamericas. (2020a). Las dos mayores empresas de torres de telecomunicaciones de México. Bnamericas, 2–5. https://www.bnamericas.com/en/news/spotlight-mexicos-2-biggest-telecom-tower-companies

Bnamericas. (2020b). Snapshot: Latin America’s main fiber backbone projects. https://www.bnamericas.com/en/features/snapshot-latin-americas-main-fiber-backbone-projects

Bravo, J. (2021). La Red Descompuesta. El Economista, 1–14. https://www.eleconomista.com.mx/opinion/La-Red-Descompuesta-20210430-0049.html

Frias, Z., Mendo, L., & Oughton, E. J. (2020). How Does Spectrum Affect Mobile Network Deployments? Empirical Analysis Using Crowdsourced Big Data. IEEE Access, 8, 190812–190821. https://doi.org/10.1109/access.2020.3031963

Ghasemi, A., & Parekh, J. (2021). Deep Learning based Localization of LTE eNodeBs from Large Crowdsourced Smartphone Datasets. IEEE Vehicular Technology Conference, 2021-April. https://doi.org/10.1109/VTC2021-Spring51267.2021.9448857

Ibarra, D. (2021). Operadores Móviles Virtuales en México 2021. Selectra, 1–7. https://selectra.mx/celular/operadores-moviles-virtuales

Instituto Federal de Telecomunicaciones [IFT]. (2017). Las Telecomunicaciones a 4 años de la Reforma Constitucional en México. http://www.ift.org.mx/sites/default/files/contenidogeneral/estadisticas/a4anosdelareforma.pdf

Instituto Federal de Telecomunicaciones [IFT]. (2019). IMT en México. 1–11. http://www.ift.org.mx/sites/default/files/imt_en_mexico_febrero_2019.pdf

Instituto Federal de Telecomunicaciones [IFT]. (2020a). Análisis sobre el mercado de Operadores Móviles Virtuales (OMVs) 2020. IFT. http://www.ift.org.mx/estadisticas/analisis-sobre-el-mercado-de-los-operadores-moviles-virtuales-omvs

Instituto Federal de Telecomunicaciones [IFT]. (2020b). Efectos y Alternativas de la Iniciativa de la Reforma a la Ley Federal de Derechos para 2021 en materia de Espectro Radioeléctrico (pp. 1–34). http://www.ift.org.mx/sites/default/files/contenidogeneral/espectro-radioelectrico/efectosyalternativasdelainiciativadereformaalaleyfederaldederechospara2021enmateriadeespectroradioel.pdf

Instituto Federal de Telecomunicaciones [IFT]. (2020c). Segundo Informe Trimestral Estadístico 2020. http://www.ift.org.mx/sites/default/files/contenidogeneral/estadisticas/ite2t2020.pdf

Instituto Federal de Telecomunicaciones [IFT]. (2021). IMT en México. http://www.ift.org.mx/sites/default/files/imt_en_mexico_2021_febrero2021.pdf

Lucas, N. (2020a). Telcel compra la banda de 3.5 GHz de Axtel y mete presión a la primera subasta de 5G en México. El Economista, 1–11. https://www.eleconomista.com.mx/empresas/Telcel-compra-la-banda-de-3.5-GHz-de-Axtel-y-mete-presion-a-la-primera-subasta-de-5G-en-Mexico-20200701-0065.html

Lucas, N. (2020b). Telefónica renuncia a sus concesiones mexicanas en las bandas de 800 MHz y PCS. El Economista, 1–13. https://www.eleconomista.com.mx/empresas/Telefonica-renuncia-a-sus-concesiones-mexicanas-en-las-bandas-de-800-MHz-y-PCS-20200101-0012.html

Martínez, C. (2020). Ante alza en precio , AT&T devuelve espectro. El Universal, 1–6. https://www.eluniversal.com.mx/cartera/ante-alza-en-precio-att-devuelve-espectro

OECD. (2020). Latin American Economic Outlook 2020: Digital Transformation for Building Back (O. Pulbishing (ed.)). https://doi.org/https://doi.org/10.1787/e6e864fb-en

Ogrenci, A. S., & Arsan, T. (2018). Transmitter source location estimation using crowd data. Computers and Electrical Engineering, 66, 127–138. https://doi.org/10.1016/j.compeleceng.2017.09.026

OpenSignal. (2019). OpenSignal: About us. https://www.opensignal.com/about/about-us

SCT. (2020). Evaluación de la Red Compartida 2020. In Gobierno de México (Vol. 53, Issue 9). https://www.gob.mx/cms/uploads/attachment/file/604427/Evaluaci_n_de_la_Red_Compartida_2020_Versi_n_FINAL_P_BLICA_.pdf

Sridhar, V., Girish, K., & Badrinarayan, M. (2021). Analysis of crowdsourced data for estimating data speeds across service areas of India. Telecommunication Systems, 76(4), 579–594. https://doi.org/10.1007/s11235-020-00736-z

Wang, H., Xie, S., Li, K., & Omair Ahmad, M. (2019). Big data-driven cellular information detection and coverage identification. Sensors (Switzerland), 19(4), 1–23. https://doi.org/10.3390/s19040937

Zeydan, E. (2021). Android vs. IOS: a comparative analysis over mobile operator infrastructures based on crowdsourced mobile dataset. Telecommunication Systems. https://doi.org/10.1007/s11235-021-00820-y

Zhang, T., Gao, J., & Cheng, J. (2017). Crowdsourced Testing Services for Mobile Apps. Proceedings - 11th IEEE International Symposium on Service-Oriented System Engineering, SOSE 2017, 75–80. https://doi.org/10.1109/SOSE.2017.28

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2022 Nova Scientia