Analyzing and modeling spatio-temporal dependence of cellular traffic at city scale

Traffic characteristics over space and time constitute an important aspect of cellular networks in consideration of resource provision, traffic engineering and system optimization. Despite recent progress in revealing temporal dynamics and spatial inhomogeneity of cellular traffic, limited knowledge about traffic dependence is gained. One of challenges comes from the absence of sustained observations at a network-wide scale. In this paper, we make an analysis on the week-long traffic generated by a large population of users in a city of China, and model traffic dependence along both space and time dimensions. The evaluation results suggest connections between spatio-temporal dependence of cellular traffic and the organization of human lives. Region differences are observed to impact traffic dependence to a great extent. Additionally, interactive knowledge between space and time enhances traffic prediction with a decrease in root-mean-square error of 2.8%∼25.2%. We believe that these achievements will benefit multiple research and development areas such as network deploying and simulation researches.