Exploiting diurnal user mobility for predicting cell transitions

Abstract

Mobility of commuters is not purely random but rather direction oriented and may be learned after monitoring user movements for a couple of business days. Exploiting movement data and context information of diurnal user movements (public transportation, vehicular users, etc.) allows for predicting cell transitions and lays the basis e.g. for designing efficient resource reservation schemes or smart resource mapping approaches. In real life scenarios, several mobile users co-travel in public transport forming data intensive moving user clusters or moving networks. Various load balancing solutions exist to manage congestion situations that could arise. However, the crucial trigger for these solutions is timely prediction of arrival of moving user clusters or moving networks into a cell. This paper presents prediction and detection schemes that exploit context information for predicting user cell transitions and resulting congestion. These schemes are utilized to anticipate the arrival of data intensive moving user groups/moving networks, which are also referred to as "hotspots", into a cell. Simulation results demonstrate robust and timely prediction of these events and their applicability for handover optimization and smart resource management even at high velocities.