Abstract
With the proliferation of bandwidth-demanding mobile applications in the era of 5G, the aggregation of a few users may lead to extremely high load in cellular base stations, producing traffic hotspot in wireless networks. Therefore the higher requirement is imposed on the flexibility of a 5G network, namely the capability of performing rapid capacity enhancement in hotspot area, which makes hotspot localization and critical prediction functions. In this paper, we proposed to localize hotspots with Gaussian Random Field (GRF)-based spatial traffic density model deduced from load data of base stations, together with the prediction with Holt-Winters. We measured the spatial traffic in a specific area within a short time span and forecasted the spatial traffic density distribution. Numeric results show the proposed approach can localize hotspot efficiently, and during traffic peak hours, hotspot prediction is of high success rate.
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