Mobility Prediction of Diurnal Users for Enabling Context Aware Resource Allocation

Mobile communication is one of the most ubiquitously used technologies in today's world, evolving towards its fifth generation (5G). Amidst increasing number of devices and traffic volume, one of the key focuses of 5G is to provide uniform service quality despite high mobility. In real world scenarios, user mobility is not random but rather direction oriented, based on its origin and destination. Further, several users exhibit repeated mobility patterns on daily basis (e.g., office goers, commuters in public transport etc.). Such mobility is termed as Diurnal mobility. Information of such diurnal mobility can assist in improving prediction accuracy of future user location (e.g., cells, routes). Knowledge of future user location will enable the designing of efficient resource management algorithms, aiming to make great service quality follow the user. In the presented work, information of diurnal mobility is used to enhance the accuracy of mobility prediction (next cell prediction as well as route prediction) in a realistic urban scenario. Further, using this context information about future routes and possible coverage holes in them, efficient resource allocation is done to sustain streaming/full buffer services, even in coverage holes. The simulation results show substantial improvements in user throughput as a result of context aware resource allocation, enabled by diurnal user mobility prediction.