To support the newly introduced multimedia services with ultra-low latency and extensive computation requirements, resource-constrained end user devices should utilize the ubiquitous computing resources available at network edge for augmenting on-board (local) processing with edge computing. In this regard, the capability of cell-free massive MIMO to provide reliable access links by guaranteeing uniform quality of service without cell edge can be exploited for seamless parallel processing. Taking this into account, we consider a cell-free massive MIMO-enabled mobile edge network to meet the stringent requirements of the advanced services. For the considered mobile edge network, we formulate a joint communication and computing resource allocation (JCCRA) problem with the objective of minimizing energy consumption of the users while meeting the tight delay constraints. We then propose a fully distributed cooperative solution approach based on multiagent deep deterministic policy gradient (MADDPG) algorithm. The simulation results demonstrate that the performance of the proposed distributed approach has converged to that of a centralized deep deterministic policy gradient (DDPG)-based target benchmark, while alleviating the large overhead associated with the latter. Furthermore, it has been shown that our approach significantly outperforms heuristic baselines in terms of energy efficiency, roughly up to 5 times less total energy consumption.

翻译：为了支持新引入的具有超低时延和大量计算需求的多媒体服务，资源受限的终端用户设备应利用网络边缘的泛在计算资源，通过边缘计算增强机载（本地）处理能力。在此背景下，无蜂窝大规模MIMO通过保证无蜂窝边缘的均匀服务质量来提供可靠接入链路的能力，可被用于实现无缝并行处理。基于此，我们考虑一个无蜂窝大规模MIMO支持的移动边缘网络，以满足高级服务的严苛要求。针对所考虑的移动边缘网络，我们提出一个联合通信与计算资源分配（JCCRA）问题，目标是在满足严格时延约束的同时最小化用户的能耗。然后，我们提出一种基于多智能体深度确定性策略梯度（MADDPG）算法的全分布式协同解决方案。仿真结果表明，所提出的分布式方法的性能已收敛到基于集中式深度确定性策略梯度（DDPG）的目标基准的性能，同时减轻了后者带来的大量开销。此外，研究表明，我们的方法在能效方面显著优于启发式基线，总能耗大约降低至五分之一。