Temporal dynamics clustering for analyzing cell behavior in mobile networks

Abstract

In recent years, management and resource allocation of mobile networks have become crucial because of a dramatic growth of mobile traffic. For Internet service providers, resource allocation is a challenging problem due to the inhomogeneous distributed nature of user demand, which increases the difficulty of configuring base stations in complicated urban environments. To manage mobile networks in an efficient way, understanding the typical usage behavior is very valuable for network operators. This paper aims at providing tools to analyze the behavior of mobile network cells in an urban environment. To achieve this goal, we propose a new time series clustering algorithm denoted as temporal dynamics clustering. Unlike conventional clustering approaches, this algorithm generates a new representation of time series by summarizing the distribution of the sequence of differences; in this way, temporal dynamics clustering creates clusters based on the variability of time series. We apply the proposed algorithm on a real-world mobile network dataset, showing its superior performance and much faster running time with respect to conventional methods such as hierarchical clustering and K-medoids. We also train neural networks on the clusters generated by temporal dynamics clustering, and the results show that the forecasting difficulty of time series is closely related to its temporal dynamics. Using this approach, we have analyzed the cell behavior with two weeks observation collected in Turin, and obtained interpretable results, in that the behavior of each cell is strongly related to the characteristics of the area and the related human activity. We also study the cell behavior in different time slots by considering time-dependent characteristic of human activity. Through experiments, we show the effectiveness of the proposed algorithm at providing a deeper understanding of traffic usage patterns in intricate urban environments.