Oct 29, 2020 · In this article, we propose a proactive mobility management approach based on group user trajectory prediction. Specifically, we introduce a ...
Dec 9, 2020 · LSTM is a powerful and flexible model for trajectory prediction, but how to speed up the model training time is a critical issue in machine ...
In this work, we present an LSTM-based mobility predictor to improve the trajectory prediction accuracy. To speed up the model convergence rate, we employ a ...
Co-authors ; Mobility management with transferable reinforcement learning trajectory prediction. Z Zhao, M Karimzadeh, L Pacheco, H Santos, D Rosário, T Braun, .
未来的移动网络将能够随时随地大规模部署移动多媒体应用。在这种情况下，移动性管理方案，例如切换和主动多媒体服务迁移，对于提高网络性能至关重要。在本文 ...
Mobility Management with Transferable Reinforcement Learning Trajectory Prediction ; Affiliation of Author(s):Beihang University, China ; Journal: ...
In this paper, we demonstrate the detrimental effects of this imbalanced skill (sub-goal) distribution and propose a novel training approach.
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Sep 24, 2023 · Mobility trajectory data is of great significance for mobility pattern study, urban computing, and city science.
In this paper we develop a predictive framework based on reinforcement learning for vehicle trajectory planning.
The study aims to provide insights into possible trade-offs between computational time and performance to support effective transfers into the real world.