Big Data Driven Mobile Cellular Networks: Modelling, Experiments, and Applications

Abstract

The proliferation and pervasive use of mobile devices results in the accumulation of massive amounts of wireless data. Mobile big data can be profitable only if suitable analytics and learning methods are utilized to extract meaningful knowledge and hidden patterns. In this article, we propose a novel mobile big data architecture consisting of five layers: the data storage layer, the data fusion layer, the data security layer, the data analysis layer and the data application layer. The functionality of each layer is presented. We consider one illustrative cases under this architecture, namely, user experience prediction by leveraging machine learning techniques. In practice, mobile big data analytics can be used for network planning and parameter dimensioning to facilitate network design, deployment and operation.