The division of one physical 5G communications infrastructure into several virtual network slices with distinct characteristics such as bandwidth, latency, reliability, security, and service quality is known as 5G network slicing. Each slice is a separate logical network that meets the requirements of specific services or use cases, such as virtual reality, gaming, autonomous vehicles, or industrial automation. The network slice can be adjusted dynamically to meet the changing demands of the service, resulting in a more cost-effective and efficient approach to delivering diverse services and applications over a shared infrastructure. This paper assesses various machine learning techniques, including the logistic regression model, linear discriminant model, k-nearest neighbor's model, decision tree model, random forest model, SVC BernoulliNB model, and GaussianNB model, to investigate the accuracy and precision of each model on detecting network slices. The report also gives an overview of 5G network slicing.

一篇关于5G网络切片的研究论文，评估了包括逻辑回归模型、线性判别模型、k最近邻模型、决策树模型、随机森林模型、SVC BernoulliNB模型和GaussianNB模型在检测网络切片准确度和精确度方面的各种机器学习技术，并提供了5G网络切片的概述。

5G 网络分片: 多个机器学习分类器的分析