Deep Mobile Path Prediction with Shift-and-Join and Carry-Ahead

Abstract

<p>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. </p>