Not Call Me Cellular Any More: The Emergence of Scaling Law, Fractal Patterns and Small-World in Wireless Networks

TL;DR

This paper analyzes the spatial traffic correlation among base stations in wireless networks, revealing scale-free, fractal, and small-world properties, and proposes a method to identify influential base stations using the collective influence algorithm.

Contribution

It introduces a correlation model based on spatial traffic, uncovers complex network properties in wireless networks, and applies a new influence localization method.

Findings

Degree distribution follows scale-free property

Networks exhibit fractality and small-world characteristics

Low-degree BSs can be more influential than high-degree ones

Abstract

In conventional cellular networks, for base stations (BSs) that are deployed far away from each other, it is general to assume them to be mutually independent. Nevertheless, after long-term evolution of cellular networks in various generations, this assumption no longer holds. Instead, the BSs, which seem to be gradually deployed by operators in a service-oriented manner, have embedded many fundamentally distinctive features in their locations, coverage and traffic loading. These features can be leveraged to analyze the intrinsic pattern in BSs and even human community. In this paper, according to large-scale measurement datasets, we build up a correlation model of BSs by utilizing one of the most important features, ie., spatial traffic. Coupling with the theory of complex networks, we make further analysis on the structure and characteristics of this traffic load correlation model. Numerical results show that the degree distribution follows scale-free property. Also the datasets unveil the characteristics of fractality and small-world. Furthermore, we apply collective influence (CI) algorithm to localize the influential base stations and demonstrate that some low-degree BSs may outrank BSs with larger degree.