Enhancing Device-to-Device direct discovery based on predicted user density patterns

Device-to-Device (D2D) direct discovery service is a key component for Proximity Services (ProSe) and D2D communications. Depending on the type of the studied network (pedestrian, vehicular, residential, industrial), large spatio-temporal fluctuation in mobile users’ density may occur inducing several patterns throughout the day. The current standards only account for fixed configurations of this service, and currently, the research into adaptive algorithms is done using analytical models and synthetic scenarios and configurations, which makes such solutions perform poorly on real systems. We propose an adaptive D2D discovery algorithm that, building upon existing work on user density prediction analytical models of the discovery process, uses historic network traces to update its operational parameters dynamically. We test the proposed algorithm and compare it to the discovery mechanism, defined in the Third Generation Partnership Project (3GPP) standards, in order to analyze the feasibility of these types of solutions. The simulation results show that the proposed algorithm strikes a balance between network utilization and time required for discovery, which is a very promising starting point for further research on this type of solutions.