利用迁移学习增强分散未来网络中移动控制的切换

Steven Platt, Berkay Demirel, M. Oliver
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引用次数: 0

摘要

传统上,在蜂窝部署中,资源管理和容量分配是由网络端控制的。随着自主性被添加到网络设计中,机器学习技术在很大程度上遵循了这一范式,受益于网络核心更高的计算能力和可用的信息环境。然而,当这些网络服务被分解或分散时,依赖于假定的网络或信息可用性级别的模型可能不再可靠地运行。本文提出了一种资源管理范式的倒置观点;在这种情况下,客户端设备执行一种学习算法,并在网络及其相应数据没有集中管理的情况下管理自己的移动性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Transition Learning to Enhance Mobile-Controlled Handover In Decentralized Future Networks
Traditionally, resource management and capacity allocation has been controlled network-side in cellular deployment. As autonomicity has been added to network design, machine learning technologies have largely followed this paradigm, benefiting from the higher compute capacity and informational context available at the network core. However, when these network services are disaggregated or decentralized, models that rely on assumed levels of network or information availability may no longer function reliably. This paper presents an inverted view of the resource management paradigm; one in which the client device executes a learning algorithm and manages its own mobility under a scenario where the networks and their corresponding data underneath are not being centrally managed.
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