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NWDAF — Data Analytics in the 5GC (Network Data Analytics)

Difficulty: Intermediate / Estimated study time: 20 min / Prerequisites: 5GC Overview and an understanding of SBA (Service Based Architecture) / Main actor: NWDAF / Main Interface: Nnwdaf

Learning objectives

After reading this chapter, you will be able to:

  • Explain that the NWDAF (Network Data Analytics Function) is "the NF that analyzes network data and provides the results (Analytics) to other NFs."
  • Diagram the flow of the NWDAF's practice of collecting data from other NFs/OAManalyzing itproviding the results to other NFs.
  • Cite the concept of the Analytics ID that identifies the kind of analytics, and representative examples (slice load, QoS Sustainability, etc.).
  • Explain the relationship in which other NFs Subscribe/Request analytics results through Nnwdaf (TS 23.288).
  • Get an overview of the idea of MTLF/AnLF (the logical split between ML training and analytics) introduced in Rel-17/18.

Prerequisites

  • 5GC Overview — the overall picture of the 5GC and the division of roles among NFs
  • An understanding of SBA (Service Based Architecture) — the framework in which NFs expose services (Nxxx) and other NFs Subscribe/Request them
  • Supplementary: NWDAF (NF details) / Representative NFs that use analytics results: PCF, AMF, SMF

The focus of this chapter

The catalog-style details of the NWDAF NF itself (its service list, deployment forms, etc.) are in the NWDAF page. The general explanation of slices is in Network Slicing. Building on those, this chapter focuses on the concept of "what the practice of Analytics (data analytics) does in the 5GC."


Why — why the network needs data analytics

The 5GC has many NFs working together, handling slices, diverse services, and a huge number of UEs. To operate the network intelligently, you need to know "what is happening now" and "what is likely to happen next."

  • Grasping the current state — which slice is congested, which NF's load is high.
  • Prediction — whether this QoS can be maintained going forward, where the UE is likely to move.
  • Anomaly detection — whether there is behavior different from normal (signs of an attack or fault).

With such "trend, prediction, anomaly" information, each NF can make better decisions. For example, if the PCF learns that "this slice will not be able to maintain QoS going forward," it can adjust policy in advance. The NF that specializes in this analytics (Analytics) is the NWDAF.

Analogy: The NWDAF is "the network's analyst." It gathers data from each department (other NFs) and the field (OAM), summarizes trends, predicts the future, and hands decision-makers (other NFs) a report saying "this is what is likely to happen." The analyst itself does not carry out operations, but its advice shapes the quality of operations.


What — Analytics and Nnwdaf

What the NWDAF provides is Analytics (analytics results). Other NFs request "I want this analysis," and the NWDAF returns the result. The service-based IF that governs this exchange of information is Nnwdaf, and the specification is consolidated in TS 23.288 (Architecture enhancements for 5G System to support network data analytics services).

Analytics ID — the name that indicates the kind of analysis

"Which analysis you want" is identified by the Analytics ID. Here are just a few representative examples (not exhaustive).

Analytics ID (representative examples) What it analyzes Example consumers
Slice Load Level The load level of a network slice NSSF, PCF
NF Load The load status of an individual NF Various NFs, OAM
UE Mobility UE mobility (where it is likely to move) AMF, SMF
UE Communication UE communication patterns (when and how much it communicates) SMF, PCF
QoS Sustainability Prediction of whether QoS can be maintained going forward PCF
Abnormal Behaviour Detection of abnormal behavior PCF, AMF

There are many more Analytics IDs

Those listed here are representative examples. In practice there are many other Analytics IDs; for the exact list and the input/output parameters of each ID, refer to TS 23.288. The detailed specification values of individual Analytics IDs are to be confirmed.

Subscribe and Request

There are broadly two ways for another NF to ask the NWDAF for analytics.

  • Subscribe — "please notify me of this analysis continuously." Each time the conditions change, the NWDAF delivers the result (Notify).
  • Request — "I want this analysis just once, now." The NWDAF returns the result only once.

For things whose situation keeps changing, such as load and QoS, use Subscribe; when you want a one-off basis for a decision, use Request.


How — the flow from data collection to analytics provision (Architecture)

The NWDAF collects data from other NFs and OAM, performs analysis, and provides the results to other NFs. The overall picture, with the NWDAF at the center, is as follows.

graph LR
    subgraph SOURCES["Data collection sources"]
        AMF_S[AMF]
        SMF_S[SMF]
        OTHER[Other NFs]
        OAM[OAM
Operations monitoring] end subgraph CONSUMERS["Consumers of analytics results"] PCF_D[PCF] AMF_D[AMF] SMF_D[SMF] NSSF_D[NSSF, etc.] end SOURCES -->|Data collection| NWDAF[NWDAF
Data analytics] NWDAF -->|Nnwdaf
Provide Analytics| CONSUMERS

Put into words, the flow is as follows.

  1. Collection — the NWDAF gathers data (load, events, statistics) from other NFs such as AMF/SMF and from OAM.
  2. Analysis — it processes the gathered data according to the Analytics ID, deriving trends, predictions, and anomalies.
  3. Provision — it returns the results via Nnwdaf to the other NFs (PCF, etc.) that Subscribed/Requested them.

Example use case: Suppose the PCF has Subscribed to the QoS Sustainability analysis from the NWDAF. When the NWDAF notifies that "the QoS in a certain area is expected to be unsustainable going forward," the PCF can use that as a basis for adjusting policy in advance (e.g., bit-rate throttling or a slice-side response). The decision-maker (PCF) acts on seeing the analyst's (NWDAF's) report.

Coordinating data collection sources (DCCF)

When many NWDAFs or consumers request the same data, collection becomes duplicated. A DCCF (Data Collection Coordination Function) that coordinates this may be involved. However, this chapter only touches on it conceptually, and its exact role and placement are to be confirmed (the TS 23.288 family).


MTLF and AnLF — the division of roles between analytics and ML training

The NWDAF was introduced in Rel-16. Initially the central function was "analyze data and produce results," but in Rel-17/18 it was expanded into a framework that handles machine learning (ML) models.

Within that, an idea of logically dividing the NWDAF's internal functions into two was introduced.

Logical function Role (concept)
MTLF (Model Training Logical Function) The side that trains and provides ML models
AnLF (Analytics Logical Function) The side that uses trained models to infer and provide analytics (Analytics)

As an image, the MTLF is "the workshop that makes models," and the AnLF is "the analytics counter that uses those models to produce predictions." This split makes it possible to scale and deploy model training and analytics provision separately.

The details of the split are conceptual only

The exact defining locations, provided services, and interaction details of the MTLF/AnLF differ by version, so when citing precisely, treat them as to be confirmed against TS 23.288. In this chapter, it is enough to grasp the concept that roles are divided into "training (MTLF)" and "analytics provision (AnLF)."


3GPP Specification

Spec Content
TS 23.288 Architecture enhancements for 5G System (5GS) to support network data analytics services (NWDAF/Analytics/Nnwdaf)

Granularity of references

NWDAF, Analytics, Nnwdaf, MTLF/AnLF, and DCCF are all specified in TS 23.288 (introduction in Rel-16 is certain). However, the detailed section numbers within the TS, the specification values of individual Analytics IDs, and the exact defining locations of MTLF/AnLF/DCCF differ by version and are to be confirmed. Behaviors not in the spec, and parts left to operator discretion, are implementation-dependent.


FAQ

Q. Does the NWDAF control the network itself?

No. The NWDAF's role is to provide analytics results (Analytics); it does not perform the control itself. Other NFs such as the PCF that receive the results use them as a basis to perform control and policy decisions. The NWDAF is the "analyst," and control is the job of the "decision-makers (other NFs)."

Q. How many kinds of Analytics ID are there?

The slice load, NF load, UE mobility, UE communication pattern, QoS Sustainability, and abnormal behavior detection listed in this chapter are representative examples; in practice there are many others. For the complete list and the inputs/outputs of each ID, refer to TS 23.288.


Summary

  • The NWDAF is the NF in the 5GC that specializes in data analytics (Analytics).
  • It collects data from other NFs/OAManalyzes itprovides the results to other NFs (PCF, etc.).
  • The provisioning IF is Nnwdaf (TS 23.288). Other NFs ask for analytics via Subscribe/Request.
  • The kind of analysis is identified by the Analytics ID (slice load, QoS Sustainability, abnormal behavior detection, and many more).
  • Introduced in Rel-16. Expanded to ML in Rel-17/18, with roles divided into training (MTLF) and analytics provision (AnLF).
  • In the analogy, it is "the network's analyst." It produces trends, predictions, and anomalies, and helps other NFs make decisions.

Comprehension check

Q1. Explain the role of the NWDAF from the perspective of the data flow (collection, analysis, provision).

The NWDAF collects data from other NFs and OAM, performs analysis according to the Analytics ID (deriving trends, predictions, and anomalies), and provides the results (Analytics) to other NFs via Nnwdaf. The NWDAF itself does not control; the other NFs (PCF, etc.) that receive the results use them for decision-making.

Q2. What is an Analytics ID? Cite one representative example and explain it.

An Analytics ID is a name that identifies "which analysis you want." For example, QoS Sustainability is an analysis that predicts whether QoS can be maintained going forward in a given area/condition, and it is mainly used by the PCF as a basis for policy adjustment. Other representative examples include slice load, NF load, UE mobility, and abnormal behavior detection; for the whole set, see TS 23.288.

Q3. Explain the difference between MTLF and AnLF at the conceptual level.

This is the idea, introduced in Rel-17/18, of logically dividing the NWDAF's internals. The MTLF (Model Training Logical Function) is the side that trains and provides ML models, and the AnLF (Analytics Logical Function) is the side that uses trained models to infer and provide analytics. Separating training and analytics provision allows them to be scaled and deployed separately. For details, to be confirmed against TS 23.288.


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