论文标题
NWDAF方法5G核心网络信号流量：分析和表征
An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization
论文作者
论文摘要
数据驱动的方法和范例已成为通过优化进行有效网络性能的有希望的解决方案。这些方法着眼于最新的机器学习技术，这些技术可以满足5G网络和明天的网络的需求，例如主动负载平衡。与基于模型的方法相反，数据驱动的方法不需要准确的模型来解决目标问题，其相关架构为可用的系统参数提供了灵活性，从而改善了移动无线网络中基于学习的算法的可行性。本文介绍的工作重点是展示5G核心（5GC）网络的工作系统原型和网络数据分析功能（NWDAF），用于将数据驱动技术的好处带入实现。网络生成数据的分析通过无监督的学习，聚类和评估这些结果作为对未来的机会和工作的见解，探索了核心网络内的交互。
Data-driven approaches and paradigms have become promising solutions to efficient network performances through optimization. These approaches focus on state-of-the-art machine learning techniques that can address the needs of 5G networks and the networks of tomorrow, such as proactive load balancing. In contrast to model-based approaches, data-driven approaches do not need accurate models to tackle the target problem, and their associated architectures provide a flexibility of available system parameters that improve the feasibility of learning-based algorithms in mobile wireless networks. The work presented in this paper focuses on demonstrating a working system prototype of the 5G Core (5GC) network and the Network Data Analytics Function (NWDAF) used to bring the benefits of data-driven techniques to fruition. Analyses of the network-generated data explore core intra-network interactions through unsupervised learning, clustering, and evaluate these results as insights for future opportunities and works.
