Monitoring Vehicular User Mobility to Predict Traffic Status and Manage Radio Resources
Mobile communication is one of the most ubiquitously used technologies in contemporary world, evolving towards
its fifth generation (5G). In day-to-day scenarios, many vehicular users avail broadband cellular services while traveling. The density of such vehicular users change dynamically in a cell and at certain sites (e.g. signal lights), traffic jams would arise frequently. Such conditions would pose high load situation to respective serving base station. As a consequence, the cell site would experience high dropping and blocking rates and subject its users to poor Quality of Experience (QoE). In this work, mobility behavior of vehicular users are analyzed and an algorithm is designed to predict traffic status of a cell. The proposed traffic prediction algorithm is a coalition strategy consisting of schemes to predict user cell transition, vehicular cluster/moving network detection, user velocity monitoring etc. The traffic status indication provided by the algorithm could be used to design efficient radio resource management (RRM) techniques. In the presented paper, this context information about traffic severity is used to pro-actively initiate load balancing at corresponding site and release resources. Further, appropriate small cells are activated/deactivated based on formation/dispersion of traffic jams respectively. The simulation results exhibit substantial reductions in dropping and blocking of users, demonstrating
improved QoE of users.