Network Data Analytics Function (NWDAF) Enhanced | RADCOM ACE

NWDAF (Network Data Analytics Function) constitutes a NF inside the 5G Core (5GC) that can orchestrate closed-loop automation in conjunction with 3rd party NFs and OAM solutions. With NWDAF, operators can provide another level of network data granularity.

As an additional step towards full network automation, operators can move to a closed-loop approach to managing their network by deploying our NWDAF solution, RADCOM ACE.

RADCOM ACE enhanced NWDAF provides continuous monitoring of each and every network dimension (slice, network function, cell site, group of UEs, or individual UEs) and per each instance within the dimension, using multiple KPIs/KQIs that measure the customer experience and network performance as required by the use case. NWDAF closed-loop use cases include:

  • Automated resolving of network issues occurring now based on real-time KPIs from the NWDAF
  • Automatic prevention of network issues predicted in the future using AI/ML predictive analytics from the NWDAF
  • Automated mitigation of anomalies detected by NWDAF AI/ML anomaly detection.


Corrective action is then selected based on the detected anomaly and root cause analysis and automatically pushed to OAM or 3rd party NFs for execution. Once the corrective action has been applied, the network can be fully monitored once again.

Implementing a closed-loop automated issue detection and solution using RADCOM NWDAF

Network slicing combined with NWDAF | RADCOM ACE

RADCOM ACE enables operators to monitor each virtual slice end-to-end, mapping every XDR/KPI/KQI to the relevant service slice to understand the overall QoE and QoS and confirm compliance to SLAs. Furthermore, it identifies the network slice instance and creates the slice utilization KPI’s that are provided to the PCF and NSSF per network slice instance.

Its multi-tenancy capabilities enable operators to provide their enterprise customers with self-monitoring capabilities of each virtual slice. As a result, hundreds or even thousands of enterprises can self-manage their slices while ensuring their isolation and complete data privacy.

RADCOM ACE can utilize each KPI and KQI to detect and predict anomalies and deviations from SLA and trigger automated closed-loop corrective action as described above. With built-in AI/ML capabilities, it can predict the future behavior of each network slice, therefore initiating an automated closed-loop corrective action that can resolve the issue before it affects subscribers. Thus, providing operators with automatic, data-driven adjustments and insights not possible to reach through manual network monitoring.

Further reading

Skip to content