Spatial-Temporal Analysis of Traffic Load Based on User Activity Characteristics in Mobile Cellular Network

Abstract

In this paper, we quantify the interactive pattern between two time series: the number of users (NoU) representing user’s activity (UA) and downlink traffic load (DTL) generated from the base station (BS). We model the characteristics of UA, and use K-means clustering algorithm to characterize the hidden spatial association pattern in the wireless cellular system. The results show that (1) there is a strong linear interaction between UA and DTL; (2) the NoU has a strong weekdays and weekends mode. (3) the results of clustering well match the reference scenario information, with the scenario recognition accuracy of 75%. We demonstrate that such approach proposed can identify the scenario of the BSes, which can help us understand the spatial temporal traffic patterns of wireless cellular system.