5G introduced modularized network functions (NFs) to support emerging services in a more flexible and elastic manner. To mitigate the complexity in such modularized NF management, automated network operation and management are indispensable, and thus the 3rd generation partnership project (3GPP) has introduced a network data analytics function (NWDAF). However, a conventional NWDAF needs to conduct both inference and training tasks, and thus it is difficult to provide the analytics results to NFs in a timely manner for an increased number of analytics requests. In this article, we propose a hierarchical network data analytics framework (H-NDAF) where inference tasks are distributed to multiple leaf NWDAFs and training tasks are conducted at the root NWDAF. Extensive simulation results using open-source software (i.e., free5GC) demonstrate that H-NDAF can provide sufficiently accurate analytics and faster analytics provision time compared to the conventional NWDAF.

翻译：5G引入了模块化的网络功能（NF），以更灵活和弹性的方式支持新兴服务。为减轻模块化NF管理中的复杂性，自动化网络运维管理不可或缺，因此第三代合作伙伴计划（3GPP）引入了网络数据分析功能（NWDAF）。然而，传统NWDAF需要同时执行推理和训练任务，因此在面对日益增多的分析请求时，难以及时向NF提供分析结果。本文提出一种分层网络数据分析框架（H-NDAF），其中推理任务分布到多个叶子NWDAF，训练任务在根NWDAF执行。基于开源软件（即free5GC）的大量仿真结果表明，与传统NWDAF相比，H-NDAF能够提供足够精确的分析结果，并显著缩短分析提供时间。