Resource Planning Based on Trajectory Prediction in Ultra-dense Networks

Inter-cell interference severely limits the network throughput and cell-edge user experience in ultra-dense networks. By using the predict average channel gains from the predicted trajectories and a radio map, with which future resources can be exploited to coordinate interference, balance the traffic load to provide evident performance gain over non-predictive counterparts. However, the existing resource planning studies mainly consider the ideal assumption of predictive information. To investigate the actual performance gain of the resource plan, we develop a trajectory prediction method and a resource planning scheme in ultra-dense networks. The simulation results show that the proposed trajectory prediction method can meet the requirement of resource planning. Furthermore, the proposed resource planning can work well under arbitrary prediction horizons. When the prediction horizon is 3 minutes, compared with the non-predictive interference management method, our resource planning can improve the user satisfaction rate by more than 45%.