Predictive Resource Allocation with Coarse-Grained Mobility Pattern and Traffic Load Information

Predictive resource allocation can exploit residual resources in wireless networks to support high throughput, improve user experience, and enhance energy efficiency. Most priori works assume that fine-grained knowledge for user trajectory and/or traffic load is known, which is hard to predict in practice. In this paper, we investigate predictive resource allocation to achieve high throughput for mobile users requesting video-on-demand (VoD) services, which employs cell-level coarse grained information. In the start of a prediction window, we only need to predict the cells the users to be associated with, the sojourn time of each user in each cell, the loads of VoD traffic and realtime traffic at each base station (BS). These information is translated into two thresholds, which are introduced to help each BS to determine when and how much data to transmit. Two-threshold-based algorithms are provided. Simulation results show that the algorithms perform closely to the optimal predictive resource allocation with perfect fine-grained information in terms of supporting high request arrival rate and improving user experience, and one algorithm even outperforms the optimal method with prediction errors.