Characterizing and Modeling Social Mobile Data Traffic in Cellular Networks

Understanding traffic characteristics in cellular networks is of great significance for better network design and performance optimization. The rapid development of various social networking applications for smart devices makes it an imperative to carry out cellular data traffic analysis further into the application level. In this paper, based on a plenty of practical mobile data traffic records, we focus on three typical application types and draw conclusions in terms of statistical characteristics and appropriate distribution model for social mobile data traffic. Firstly, the universal existence of burstiness and self-similarity is demonstrated by testing traffic series at different time scales. Afterwards, α-stable distributions are used to model traffic series benefiting from their internal burstiness and self- similarity. The minor fitting errors verify the validity of α-stable model and a preliminary traffic prediction shows the usefulness of α-stable model for further traffic analysis.