  Nowadays mobile communication is growing fast in the 5G communication
industry. With the increasing capacity requirements and requirements for
quality of experience, mobility prediction has been widely applied to mobile
communication and has becoming one of the key enablers that utilizes historical
traffic information to predict future locations of traffic users, Since
accurate mobility prediction can help enable efficient radio resource
management, assist route planning, guide vehicle dispatching, or mitigate
traffic congestion. However, mobility prediction is a challenging problem due
to the complicated traffic network. In the past few years, plenty of researches
have been done in this area, including Non-Machine-Learning (Non-ML)- based and
Machine-Learning (ML)-based mobility prediction. In this paper, firstly we
introduce the state of the art technologies for mobility prediction. Then, we
selected Support Vector Machine (SVM) algorithm, the ML algorithm for practical
traffic date training. Lastly, we analyse the simulation results for mobility
prediction and introduce a future work plan where mobility prediction will be
applied for improving mobile communication.
