Mobility-Aware User Association and Computation Offloading in Ultra-Dense Networks

The integration of mobile edge computing (MEC) into ultra-dense network (UDN) has emerged as a promising solution for providing uninterrupted task offloading services to computation-demanding mobile devices (MDs), leveraging densely deployed MEC servers. However, the mobility of MDs can significantly affect the computation offloading decision and system performance in UDN-MEC. To address this issue, we develop a joint optimization algorithm for mobility-aware user association and computation offloading to minimizize the total system cost, including delay and energy consumption in UDN-MEC. Specifically, a polynomial fitting algorithm based on an extended Kalman filter (PFA-EKF) is designed to predict the nonlinear moving trajectories of the MDs. Furthermore, we present a mobility-aware user-association rule based on a breadth-first search (UAR-BFS) that can realize balanced matching offloading in the overlapping coverage area of densely deployed small base stations. Accordingly, we propose an improved deep deterministic policy gradient-based greedy algorithm (IDDPG-GA) to address the joint optimization problem of multiuser dynamic offloading and resource allocation, in which an intelligently adjusted bias factor is proposed to enhance collaborative behavior among MDs. Extensive numerical results and comparisons demonstrate the promising performance improvements of the proposed scheme compared with existing benchmark schemes in various scenarios.