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Where to Go Next? : A Realistic Evaluation of AI-Assisted Mobility Predictors for HetNets
بعد کجا برویم؟ : ارزیابی واقعی پیش بینی کننده های تحرک با کمک هوش مصنوعی برای HetNets-2020 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 3GPPcompliant
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 0dB to 4dB. Index Terms: Mobility prediction | AI | self- organizing networks (SON) | Deep Neural Networks | XGBoost | HetNets |
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