论文标题

5G网络以外的计算卸载:分布式学习框架和应用程序

Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications

论文作者

Chen, Xianfu, Wu, Celimuge, Liu, Zhi, Zhang, Ning, Ji, Yusheng

论文摘要

面对合并无线通信和多访问边缘计算(MEC)的趋势,本文研究了《超越第五代网络》中的计算卸载。为了解决源自MEC系统中不确定性的技术挑战和有限资源的共享,我们将计算卸载问题提出为多代理马尔可夫决策过程,为此提出了分布式学习框架。我们提出了一项有关计算卸载中资源编排的案例研究,以展示在拟议框架下开发的在线分布式增强学习算法的潜力。实验结果表明,我们的学习算法优于基准资源编排算法。此外,我们概述了值得深入调查的研究指示,以最大程度地减少时间成本,这是阻止实施拟议的分布式学习框架的主要实际问题之一。

Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in the beyond fifth-generation networks. To address the technical challenges originating from the uncertainties and the sharing of limited resource in an MEC system, we formulate the computation offloading problem as a multi-agent Markov decision process, for which a distributed learning framework is proposed. We present a case study on resource orchestration in computation offloading to showcase the potentials of an online distributed reinforcement learning algorithm developed under the proposed framework. Experimental results demonstrate that our learning algorithm outperforms the benchmark resource orchestration algorithms. Furthermore, we outline the research directions worth in-depth investigation to minimize the time cost, which is one of the main practical issues that prevent the implementation of the proposed distributed learning framework.

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