Optimal predictive resource allocation: Exploiting mobility patterns and radio maps

Resource Allocation (RA) in cellular networks is a challenging problem due to the demanding user requirements and limited network resources. Moreover, mobility results in channel gains that vary significantly with time. However, since location and received signal strength are correlated, user mobility patterns can be exploited to predict the data rates they will experience in the future. In this paper, we show that with such predictions, long-term RA plans that span multiple cells can be made. We formulate an optimal Predictive Resource Allocation (PRA) framework for a network of cells as a linear programming problem for three different objectives. Presented numerical results provide a benchmark of the PRA performance in realistic and random user mobility scenarios. Significant network and user satisfaction gains are observed compared to RA schemes that do not utilize any predictions.