In this paper, with the aim of addressing the stringent computing and quality-of-service (QoS) requirements of recently introduced advanced multimedia services, we consider a cell-free massive MIMO-enabled mobile edge network. In particular, benefited from the reliable cell-free links to offload intensive computation to the edge server, resource-constrained end-users can augment on-board (local) processing with edge computing. To this end, we formulate a joint communication and computing resource allocation (JCCRA) problem to minimize the total energy consumption of the users, while meeting the respective user-specific deadlines. To tackle the problem, we propose a fully distributed solution approach based on cooperative multi-agent reinforcement learning framework, wherein each user is implemented as a learning agent to make joint resource allocation relying on local information only. The simulation results demonstrate that the performance of the proposed distributed approach outperforms the heuristic baselines, converging to a centralized target benchmark, without resorting to large overhead. Moreover, we showed that the proposed algorithm has performed significantly better in cell-free system as compared with the cellular MEC systems, e.g., a small cell-based MEC system.
翻译:本文旨在解决最近推出的先进多媒体服务的严格计算和服务质量要求,我们考虑采用无细胞的大型移动边缘网络,特别是从可靠的无细胞连接到卸载大量计算到边缘服务器的卸载大量计算,资源限制的最终用户可以用边缘计算来增加机载(当地)处理。为此,我们制定了联合通信和计算资源分配(JCCRA)问题,以尽量降低用户的能源消耗总量,同时满足各自的用户特定期限。为解决这一问题,我们提议采用基于合作性多剂强化学习框架的完全分布式解决方案,其中每个用户都作为学习的代理实施,仅依靠当地信息进行联合资源分配。模拟结果表明,拟议的分布式方法的性能超越了超常基线,凝聚到一个集中的目标基准,不诉诸大型管理。此外,我们表明,拟议的算法在无细胞系统方面比手机的MEC系统(例如以小细胞为基础的MEC系统)表现要好得多。