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

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. Published under an exclusive license by AIP Publishing.

Automated summary

This study aims to provide theoretical and decision-making support for congestion issues in mobile communication systems by analyzing the network of high-traffic events. By mapping mobile communication spatiotemporal data to a weighted network, the associations among the occurrence times of high-traffic events can be mined through topological analysis. The research findings indicate that the event synchronization network exhibits characteristics of a small-world network.