.png){kind=icon}
返回
A Machine-Learning-Based Framework for Optimizing the Operation of Future Networks
.png){kind=icon}
OA
AI
DOI：10.1109/MCOM.001.1900601.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
5G and beyond are not only sophisticated and difficult to manage, but must also satisfy a wide range of stringent performance requirements and adapt quickly to changes in traffic and network state. Advances in machine learning and parallel computing underpin new powerful tools that have the potential to tackle these complex challenges. In this article, we develop a general machinelearning- based framework that leverages artificial intelligence to forecast future traffic demands and characterize traffic features. This makes it possible to exploit such traffic insights to improve the performance of critical network control mechanisms, such as load balancing, routing, and scheduling. In contrast to prior works that design problem-specific machine learning algorithms, our generic approach can be applied to different network functions, allowing reuse of existing control mechanisms with minimal modifications. We explain how our framework can orchestrate ML to improve two different network mechanisms. Further, we undertake validation by implementing one of these, mobile backhaul routing, using data collected by a major European operator and demonstrating a 3? reduction of the packet delay compared to traditional approaches.
Keyword:
Feature extraction
Routing
Optimization
5G mobile communication
Computational modeling
Wireless networks
Pipelines
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.2
论文数:
6.9K
被引数:
2.2W
机构
引用论文
The effect of azathioprine on gastric mucosal histology and acid secretion in chronic gastritis硫唑嘌呤对慢性胃炎胃粘膜组织学和酸分泌的影响.png)
GutOAAI
GutIF0
OAAI分享收藏
查看更多内容