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Private and Open Data in Asia: A Regional Guide.
Radio Access Network (RAN) network planning is continuously analyzing network capacity and behavior in order to identify the base stations (BS) whose performance is not satisfactory. For this purpose, network planning involves many tasks: measurement of current network traffic and the network performance itself, prediction of future network demands, determination of the optimal allocation of capacity resources according to the goals established, validation, and implementation.
However, operators have realized that a RAN upgrade should not only be based on network data, but should also consider customer data, in order to know such details as:
This new approach requires working with big data, which adds a new challenge; but it improves network upgrade efficiency by providing new customer and device insights.
In this presentation, Vianney Martinez will focus on the 3G network of a mobile operator, and look at three use cases that involve a decision regarding which BSs should be upgraded (e.g. by adding additional carrier frequencies); provide 4G coverage to offload 3G traffic; or stay as they are based on the customer usage profile, quality requirements, and affected revenue. The presentation will also highlight the advantage of using Hadoop for these analyses, and the real possibility of having results with high accuracy.
Vianney Martínez Alcántara works for Datameer in big data for telecom companies. In 2014 she graduated from the Technical University of Munich in Communications Systems. Her master’s thesis is in the space of network optimization and big data. Vianney has a bachelor’s degree in electronic engineering from the Monterrey Institute of Technology and Higher Education. Born in Mexico City, Vianney believes that education can change minds and the world. Since 2006 she has been involved in activities related to education in her home country, helping kids to reinforce their knowledge at school.
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