Identifying the spatiotemporal organization of high-traffic events in a mobile communication system using event synchronization and complex networks

Abstract

In mobile communication systems, congestion is related to high-traffic events (HTEs) that occur in the coverage areas of base stations. Understanding, recognizing, and predicting these HTEs and researching their occurrence rules provides theoretical and decision-making support for preventing system congestion. Communication sectors are regarded as nodes, and if HTEs occur synchronously among sectors, then the corresponding nodes are connected. The total number of synchronous HTEs determines the edge weights. The mobile-communication spatiotemporal data are mapped to a weighted network, with the occurrence locations of HTEs as the basic elements. Network analysis provides a structure for representing the interaction of HTEs. By analyzing the topological features of the event synchronization network, the associations among the occurrence times of HTEs can be mined. We find that the event synchronization network is a small-world network, the cumulative strength distribution is exponential, and the edge weight obeys a power law. Moreover, the node clustering coefficient is negatively correlated with the node degree. A congestion coefficient based on several topological parameters is proposed, and the system congestion is visualized. The congestion coefficient contains information about the synchronous occurrence of HTEs between a sector and its neighbors and information about the synchronous occurrence of HTEs among its neighbors. For the mobile communication system considered in this study, the congestion coefficient of a large number of sectors is small and the risk of system congestion is low.

A Method to Explore the Synchronous Changes of High-Traffic Events Based on Dynamic Networks

“…Using complex networks to study mobile communication complex systems and analyzing the time and space requirements of mobile users for data services will help operators plan base stations. Wang et al [8] used the event synchronization method to study the similarity between high traffic base stations to visualize the system congestion. To optimize network resource allocation Zhang et al [9] proposed an optimization algorithm for complex network communication resource allocation based on an improved neural network.…”