Long-Term Traffic Characterization in a Large-Scale Cellular Network Based on Limited Time Series

Understanding long-term (e.g., one year) traffic characteristics of cellular base stations (BSs) is of great value to network operators. However, there are rarely modeling results about them. In this paper, we characterize the long-term (i.e., one year) traffic patterns of thousands of BSs in a large-scale cellular network of China. We first find that the traffic distribution among BSs is highly skewed and BSs' traffic varies dramatically in a year. In order to cluster meaningful BS traffic patterns, we use a new clustering method, in which a BS's monthly traffic is represented by its rank in the BS's traffic time series. In this method, we find that the thousands of BSs have six typical traffic patterns, and the patterns are interpretable: they are clearly related to two important events in China: 1) Spring Festival when a lot of people return hometown to reunion with family, 2) Double 11 Shopping Festival when a lot of people shop online. They are also related to the BSs' geographic location and address information. Our measurement and analysis results provide useful information for cellular network providers to understand and plan their networks, and our clustering method can be applied in similar traffic pattern mining problems.