Publication Type

Journal Article

Version

acceptedVersion

Publication Date

9-2019

Abstract

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviours and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatiotemporal dependency even among distant cell towers, which is largely overlooked in previous works. To explicitly characterize and effectively model the spatio-temporal dependency of urban cellular traffic, we propose a novel decomposition of in-cell and inter-cell data traffic, and apply a graph-based deep learning approach to accurate cellular traffic prediction. Experimental results demonstrate that our method consistently outperforms the state-of-the-art time-series based approaches and we also show through an example study how the decomposition of cellular traffic can be used for event inference.

Keywords

Cellular networks, Internet, Mobile handsets, Monitoring, Poles and towers, Predictive models, Urban areas

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Mobile Computing

Volume

18

Issue

9

First Page

2190

Last Page

2202

ISSN

1536-1233

Identifier

10.1109/TMC.2018.2870135

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TMC.2018.2870135

Download

Share

COinS