Mobility context awareness to improve Quality of Experience in traffic dense cellular networks

Mobile communication is one of the most ubiquitously used technologies in contemporary world, evolving towards its fifth generation (5G). The data traffic demand by moving users (vehicular users) has been constantly increasing. It is a key challenge in 5G to satiate the requirements of such moving users and provide good Quality of Experience (QoE), despite high mobility and traffic demand. In day-to-day life, there are several practical instances where cellular network is subjected to high load situation due to vehicular users. Groups of mobile users travel together (e.g. public transport) forming a moving network and pose congestion to cells they enter. Further, density of vehicular users change dynamically in a cell and at certain sites (e.g. signal lights), traffic jams arise frequently. Such scenarios would pose high load situation to respective serving base station. As a consequence, the cell site would experience high dropping and blocking of users and subject them to poor QoE. This work emphasizes on building mobility context awareness to alleviate such situations in traffic dense cellular networks. The strategies to predict user-cell transition are discussed and an algorithm to predict severity of vehicular user traffic is designed. Based on these mobility context information, suitable radio resource management (RRM), namely mobility load balancing and small cell activation/deactivation are pro-actively triggered. The simulation results exhibit substantial reductions in dropping and blocking of users, demonstrating improved QoE of users, despite high mobility and data demand.2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works