A Framework Enabling Spatial Analysis of Mobile Traffic Hot Spots

An enormous increase in data traffic demanded by mobile users calls for efficient deployment strategies such as multi-layer heterogeneous networks. However, placing small cells at the desired locations to offload as much traffic as possible from overlaying macro cells is a crucial task. In this regard, geo-location and user equipment positioning techniques help obtain spatial distributions of user locations and their respective traffic volumes. In this paper, we provide a tool capable of reducing errors that stem from spatial discretization of traffic data and that can autonomously detect hot spots given a certain threshold. Based on geo-located traffic in a 3G network in a dense urban city, we find that traffic in the area is approximately log-normally distributed and that the size of traffic hot spots are approximately Weibull distributed. Based on our statistical findings, we observe that utilizing 4 small cells per km² covering 3.2% of the total area and around 34% of the total traffic volume is a very meaningful deployment strategy; however, deploying more small cells in larger hot zones becomes increasingly costly in terms of the ratio of area covered and traffic demand serviced.