Spatio-Temporal Mamba for User Mobility Prediction in Mobile Edge Computing

Abstract

In mobile edge computing (MEC), frequent server handovers due to user mobility increase latency and degrade quality of service (QoS). This study enhances MEC service stability by predicting user mobility for efficient server transitions. The proposed spacio-temporal (ST)-Mamba model combines Mamba (state-space encoder) and a gated recurrent unit (GRU) in parallel to capture both long-term and short-term dependencies, while Fourier feature embedding enriches spatial-temporal representation. Experiments show that ST-Mamba achieves about 9-10% lower root mean square error (RMSE) and mean absolute error (MAE) than long short-term memory (LSTM), GRU, and Transformer baselines, with statistically significant improvements confirmed by Welch's $t$-test. These results demonstrate that hybrid state space model (SSM)-RNN architectures are promising for mobility-aware QoS optimization in MEC, with future work extending to real-world and multi-user settings.