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Deep Mobile Path Prediction with Shift-and-Join and Carry-Ahead
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  • Syed M. Raza ,
  • Hyunseung Choo ,
  • Moonseong Kim
Sungkyunkwan University

Corresponding Author:[email protected]

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Syed M. Raza
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Hyunseung Choo
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Moonseong Kim
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Mobile user path prediction is a prerequisite for mobility-aware efficient task offloading in Multi-access Edge Computing (MEC), ultra-low latency services, and many other resource management operations. This manuscript addresses the mobile user path prediction by proposing two Deep Learning (DL) driven models that use Long Short-Term Memory (LSTM) to process time-series network data. The training and evaluation of the proposed models are done using our collected and preprocessed dataset which has been made a publicly learnable resource.
Jun 2023Published in IEEE Transactions on Cognitive Communications and Networking volume 9 issue 3 on pages 811-825. 10.1109/TCCN.2023.3242376