Proactive Mobility Management With Trajectory Prediction Based on Virtual Cells in Ultra-Dense Networks

Abstract

Ultra-dense networking (UDN) is a promising technology to improve the network capacity in the next-generation mobile communication system. The virtualization paradigm is tightly integrated into UDN to address the problem of interference management. However, mobility management based on virtual cells meets significant challenges in UDN due to the frequent handovers and massive signaling overhead. These problems become severe for vehicles owing to their high-speed movement. In this paper, driven by trajectory prediction using a real-world vehicle mobility dataset, we propose a proactive mobility management solution based on virtual cells. Four modules are designed in the centralized Software-Defined Networking controller to support the proposed solution. The proposed LSTM-DR framework predicts the next locations of vehicles by integrating Long Short-Term Memory (LSTM) networks and Dead Reckoning (DR) method. The active gNBs selection algorithm selects the serving gNBs to form virtual cells according to predicted locations and mobility preferences. The corresponding signaling procedure is then carefully designed to further reduce the signaling overhead. Simulation results show that the proposed prediction framework can achieve higher accuracy and robustness in trajectory prediction. The proposed proactive solution reduces the handover frequency and handover failure rate and thereby saves the handover signaling overhead significantly.