Behaviors of High-Frequency Subscribers in Cellular Data Networks

Abstract

Cellular networks operate under restrictive constraints of resources including radio channel capacity and network processing capability. The tremendous growth in the cellular data network usage brings operators with unprecedented signaling overloads and threatens the stability of the network. High-frequency subscribers, who produce low data volume but cause high signaling overhead, are extremely resource-inefficient. For subscribers who activate more than 5 sessions per hour, they only account for 1.19% of the total subscribers and generate about 3.81% data traffic but consume roughly 19.46% of the signaling resources, resulting in the inconsistent signaling-data bandwidth consumptions. Understanding the characteristics of those users has an important significance of capacity design and optimal allocation of resources. A lack of understanding of this active group potentially leads to low network performance and security threats. In this paper, we perform the first city-wide, large-scale investigation of high-frequency subscribers. By applying a set of novel approaches, such as pattern extraction and user behavior rebuilding, we observed that high-frequency subscribers correspond to a lower percentage of none-pattern traffic, showing positive correlation between access regularity and session activation frequency. Besides, we found that amount of high-frequency subscribers has abnormal behaviors, resulting in unwanted signaling loads. We demonstrate that our findings have significant implications on network optimization.