Hierarchical Network Data Analytics Framework for B5G Network Automation: Design and Implementation
This work addresses network automation complexity for 5G/B5G operators, though it appears incremental as it builds on existing NWDAF standards.
The authors tackled the challenge of timely analytics provision in 5G networks by proposing a hierarchical framework (H-NDAF) that distributes inference tasks to leaf nodes and centralizes training at a root node, achieving faster analytics provision time compared to conventional methods.
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.