Forecast scheduling and its extensions to account for random events

Technology evolutions make possible the use of Geo-Localized Measurements (GLM) for performance and quality of service optimization thanks to the Minimization of Drive Testing (MDT) feature. Exploiting GLM in radio resource management is a key challenge in future networks. The Forecast Scheduling (FS) concept that uses GLM in the scheduling process has been recently introduced. It exploits long term time and spatial diversity of vehicular users in order to improve user throughputs and quality of service. In a previous paper we have formulated the FS as a convex optimization problem namely the maximization of an α-fair utility function of the cumulated downlink data rates of the users along their trajectories. This paper proposes an extension for the FS model to take into account different types of random events such as arrival and departure of users and uncertainties in the mobile trajectories. Simulation results illustrate the significant performance gain achieved by the FS algorithms in the presence of random events.