# Hierarchical Network Data Analytics Framework for B5G Network Automation: Design and Implementation

arXiv: https://arxiv.org/abs/2309.16269 · HTML: https://mlsanity.com/paper/2309.16269/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [A Formal Analysis of 5G Authentication](https://mlsanity.com/paper/1806.10360/) (2018)

## Built on by (most cited first, 1 of 1)

- [Towards Network Data Analytics in 5G Systems and Beyond](https://mlsanity.com/paper/2506.04860/) (2025)

## Abstract (arXiv)

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.