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.

翻译：在传统基于蜂窝的移动边缘计算（MEC）中，位于小区边缘的用户容易受到严重的小区间干扰和信号衰减影响，导致吞吐量低下甚至传输中断。这种边缘效应严重阻碍了任务向MEC服务器的卸载。为解决该问题，我们提出了用户中心移动边缘计算（UCMEC），这是一种集成了用户中心传输的新型MEC架构，能够为任务卸载提供高吞吐量和可靠通信。然后，我们在UCMEC中联合考虑任务卸载、功率控制和计算资源分配，构建了一个优化问题，旨在获得长期平均总时延方面的最优性能。为解决这一复杂问题，我们基于多智能体深度强化学习（MADRL）和凸优化提出了两种去中心化联合优化方案，同时考虑了网络节点间的合作与非合作情形。仿真结果表明，与传统的基于蜂窝的MEC相比，所提出的UCMEC方案可将上行链路传输速率最高提升343.56%，并将长期平均总时延最多降低45.57%。