Mobility prediction Based on Machine Learning Algorithms
Konferenz: Mobilkommunikation - 25. ITG-Fachtagung
03.11.2021 - 04.11.2021 in Osnabrück
Seiten: 5Sprache: EnglischTyp: PDF
Wang, Donglin; Partani, Sanket; Qiu, Anjie; Schotten, Hans D. (University of Kaiserslautern, Germany)
Zhou, Qiuheng (German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany)
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, Support Vector Machine (SVM) algorithm is the ML algorithm we selected 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.