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tract. This paper considers the use of clustering techniques to learn the mobility patterns existing in a cellular network. These patterns are materialized in a database of prototype trajectories obtained after having observed multiple trajectories of mobile users. Both K-means and Self-Organizing Maps (SOM) techniques are assessed. Different applicability areas in the context of Self-Organizing Networks (SON) for 5G are discussed and, in particular, a methodology is proposed for predicting the trajectory of a mobile user. Keywords: Clustering


Cellular networks
Mobility patterns

1 Introduction The new generation of mobile and wireless systems, known as 5th Generation (5G), intends to provide solutions to the continuously increasing demand for mobile broadband services associated with the massive penetration of wireless equipment while at the same time supporting new use cases associated to customers of new market segments and vertical industries (e.g., e-health, automotive, energy). As a result, the vision of the future 5G Radio Access Network (RAN) corresponds to a highly heterogeneous network with unprecedented requirements in terms of capacity, latency or data rates, as identified in different fora [1, 2]. To cope with this heterogeneity and complexity, the RAN planning and optimization processes can benefit at a large extent from exploiting cognitive capabilities that embrace knowledge and intelligence. In this direction, legacy systems already started the automation in the planning and optimization processes through Self-Organizing Network (SON) functionalities [3]. In 5G, considering also the advent of big data technologies [4], it is envisioned that SON can be further evolved towards a more proactive approach able to exploit the huge amount of data available by a Mobile Network Operator (MNO) and to incorporate additional dimensions coming from the characterization of end-user experience and end-user behavior [5]. Then, SON can be enhanced through Artificial Intelligence (AI)based tools, able to smartly process input data from the environment and come up with knowledge that can be formalized in terms of models and/or structured metrics that represent the network behavior. This will allow gaining in-depth and detailed knowledge about the whole 5G ecosystem, understanding hidden patterns, data structures and relationships, and using them for a more efficient network management [6]. © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved L. Iliadis and I. Maglogiannis (Eds.): AIAI 2016, IFIP AICT 475, pp. 686–696, 2016. DOI: 10.1007/978-3-319-44944-9_61

On Learning Mobility Patterns in Cellular Networks

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AI-based SON involves three main stages [6]: (i) the acquisition and pre-processing of input data exploiting the wide variety of available data sources; (ii) the knowledge discovery that smartly processes the input data to come up with exploitable knowledge models that represent the network/user behavior; and (ii