Where to Go Next?: A Realistic Evaluation of AI-Assisted Mobility Predictors for HetNets

Abstract

5G is considered as the ecosystem to abet the ever growing number of mobile devices and users requiring an unprecedented amount of data and highly demanding Quality of Experience (QoE). To accommodate these demands, 5G requires extreme densification of base station deployment, which will result in a network that requires overwhelming efforts to maintain and manage. User mobility prediction in wireless communications can be exploited to overcome these foregoing challenges. Knowledge of where users will go next enables cellular networks to improve handover management. In addition, it allows networks to engage in advanced resource allocation and reservation, cell load prediction and proactive energy saving. However, anticipating the movement of humans is, in itself, a challenge due to the lack of realistic mobility models and insufficiencies of cellular system models in capturing a real network dynamics. In this paper, we have evaluated Artificial Intelligence (AI)-assisted mobility predictors. We model mobility prediction as a multi-class classification problem to predict the future base station association of the mobile users using Extreme Gradient Boosting Trees (XGBoost) and Deep Neural Networks (DNN). Using a realistic mobility model and a 3GPP-compliant cellular network simulator, results show that, XGBoost outperforms DNN with prediction accuracy reaching up to 95% in a heterogeneous network (HetNet) scenario with shadowing varied from OdB to 4dB.