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Title: Predicción de tráfico en redes móviles mediante Deep Learning
Author: Gran Josa, José Manuel
Director: Vilajosana i Guillén, Xavier
Tutor: López Vicario, José
Keywords: deep learning
Issue Date: Jan-2019
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: The goal of this paper is to present a study for network traffic prediction based on Deep Learnig techniques. With the predictions obtained it is pretended to optimize the virtualized elements of the Cloud RAN mobile access network architecture. Firstly, concepts related to the subject of study will be presented, such as virtualization, 5G and Cloud RAN architecture. They can be seen in detail, as they are part of the context of this study on data prediction. In order to reach this data prediction target, we also be presented tools used in the development of predictive models. We talk about Python, Pandas, Numpy, Matplotlib and Tensorflow. The last one, will allow to develop the models based on Deep Learning. The main algorithms that can be implemented to make the predictions will be analyzed in detail. It will also be considered the preprocessing of the data that can be implemented in order to make those predictions. We will introduce training, validation and test sets and concepts such as underfitting and overrfitting. The results obtained with the various methods of predicted data implemented will be shown. We will see how we get more accurate results using neural networks. The purpose of this prediction work is successfully concluded for its possible application on virtualized elements of the Cloud RAN to optimize its resources and ensure its capabilities.
Language: Spanish
Appears in Collections:Bachelor thesis, research projects, etc.

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  • Gracias
    Commenter: Anonymous
    Date: 16-Feb-2020 20:44:16
    Soy Nicolas de Argentina, trabajo en Telecom en ingeneiría de despliegue de 5G, IoT, RAN SHARING, etc en el Core de Paquetes Movil. Tu trabajo fue muy revelador, yo estaba investigando TensorFlow para modelar los patrones de trafico de la red, y poder inferir incidentes y desvios, y tu implementación funciona de maravillas, lo estoy tomando de referencia para mis investigaciones.

    Saludos y gracias nuevamente, Nicolas

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