Mobility Management with Transferable Reinforcement Learning Trajectory Prediction

Zhao, Zhongliang; Karimzadeh, Mostafa; Pacheco, Lucas; Santos, Hugo; Rosário, Denis; Braun, Torsten; Cerqueira, Eduardo (29 October 2020). Mobility Management with Transferable Reinforcement Learning Trajectory Prediction. IEEE Transactions on Network and Service Management, 17(4), pp. 2102-2116. IEEE 10.1109/TNSM.2020.3034482

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Future mobile networks will enable the massive deployment of mobile multimedia applications anytime and anywhere. In this context, mobility management schemes, such as handover and proactive multimedia service migration, will be essential to improve network performance. In this article, we propose a proactive mobility management approach based on group user trajectory prediction. Specifically, we introduce a mobile user trajectory prediction algorithm by combining the Long-Short Term Memory networks (LSTM) with Reinforcement Learning (RL) to automate the model training procedure. We further develop a group user trajectory predictor to reduce prediction calculation overheads of users with similar movement patterns. To validate the impact of the proposed mobility management approach, we present a virtual reality (VR) service migration scheme built on the top of the proactive handover mechanism that benefits from trajectory predictions. Experiment results validate our predictor’s outstanding accuracy and its impacts on enhancing handover and service migration performance to provide quality of service assurance.

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