loading page

Deep Mobile Path Prediction with Shift-and-Join and Carry-Ahead
  • +1
  • HUIGYU YANG ,
  • Syed M. Raza ,
  • Hyunseung Choo ,
  • Moonseong Kim
HUIGYU YANG
Sungkyunkwan University

Corresponding Author:[email protected]

Author Profile
Syed M. Raza
Author Profile
Hyunseung Choo
Author Profile
Moonseong Kim
Author Profile

Abstract

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