论文标题
使用过渡学习来增强分散的未来网络中移动控制的交接
Using Transition Learning to Enhance Mobile-Controlled Handoff In Decentralized Future Networks
论文作者
论文摘要
传统上，资源管理和容量分配已受到蜂窝部署的控制网络端。由于已在网络设计中添加了自主性，因此机器学习技术在很大程度上遵循了此范式，因此受益于网络核心可用的更高的计算能力和信息环境。但是，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。本文介绍了资源管理范式的倒置视图；客户设备执行学习算法并在没有集中管理的网络及其相应数据的情况下管理其自身移动性的一个。
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.
