  In the traditional cellular-based mobile edge computing (MEC), users at the
edge of the cell are prone to suffer severe inter-cell interference and signal
attenuation, leading to low throughput even transmission interruptions. Such
edge effect severely obstructs offloading of tasks to MEC servers. To address
this issue, we propose user-centric mobile edge computing (UCMEC), a novel MEC
architecture integrating user-centric transmission, which can ensure high
throughput and reliable communication for task offloading. Then, we formulate
an optimization problem with joint consideration of task offloading, power
control, and computing resource allocation in UCMEC, aiming at obtaining the
optimal performance in terms of long-term average total delay. To solve the
intractable problem, we propose two decentralized joint optimization schemes
based on multi-agent deep reinforcement learning (MADRL) and convex
optimization, which consider both cooperation and non-cooperation among network
nodes. Simulation results demonstrate that the proposed schemes in UCMEC can
significantly improve the uplink transmission rate by at most 343.56% and
reduce the long-term average total delay by at most 45.57% compared to
traditional cellular-based MEC.
