  Ultra-dense network deployment has been proposed as a key technique for
achieving capacity goals in the fifth-generation (5G) mobile communication
system. However, the deployment of smaller cells inevitably leads to more
frequent handovers, thus making mobility management more challenging and
reducing the capacity gains offered by the dense network deployment. In order
to fully reap the gains for mobile users in such a network environment, we
propose an intelligent dual connectivity mechanism for mobility management
through deep learning-based mobility prediction. We first use LSTM (Long Short
Term Memory) algorithm, one of deep learning algorithms, to learn every user
equipment's (UE's) mobility pattern from its historical trajectories and
predict its movement trends in the future. Based on the corresponding
prediction results, the network will judge whether a handover is required for
the UE. For the handover case, a dual connection will be established for the
related UE. Thus, the UE can get the radio signal from two base stations in the
handover process. Simulation results verify that the proposed intelligent dual
connectivity mechanism can significantly improve the quality of service of
mobile users in the handover process while guaranteeing the network energy
efficiency.
