How It Works
NWDAF works from network-side data and produces analytics for network and application functions to consume. SA4's studies look at applying AI/ML directly to media processing and delivery. The sections below cover NWDAF's own structure and SA4's study work.
Key specifications: 3GPP TS 23.288 (NWDAF architecture, SA2), TR 26.847 (Evaluation of AI and ML in 5G media services).
NWDAF: network-side analytics (SA2)
The Network Data Analytics Function (NWDAF) is the 5G core function through which network functions and other data sources expose measurements, and through which consumers request analytics or predictions derived from that data. TS 23.288 splits the NWDAF's internal role in two:
- AnLF (Analytics Logical Function): produces the analytics or prediction output that a consumer (for example the PCF, for QoS policy, or an Application Function such as the 5GMS AF) subscribes to or requests.
- MTLF (Model Training Logical Function): trains the ML models that an AnLF uses for inference, and can expose those models to other NWDAF instances.
Splitting training from inference lets a model be trained once (potentially on a different NWDAF instance, closer to where the training data volume is largest) and reused for inference wherever it is needed. For a media session, a typical consumer-facing output is a load or QoS prediction that a function such as the 5GMS AF can act on ahead of time, for example by adjusting a bitrate ceiling before congestion actually hits. See Standards: 5G Media Streaming (5GMS) for where the 5GMS AF sits in that path.
Data collection for NWDAF
TS 26.531, clause 4.1: “Clause 6.2.8 of TS 23.288 [4] envisages a set of high-level procedures by which data is collected by a Network Data Analytics Function (NWDAF) from UE Application(s) via an intermediary Application Function that provides an Event Exposure Service to event consumers.” The project for it is UE Data Collection, Reporting and Event Exposure.
The SA4 AI/ML media studies: TR 26.927 and TR 26.847
SA4's study of applying AI/ML techniques directly to media processing and delivery, as distinct from the network-analytics use of AI/ML in NWDAF above, is captured in two companion technical reports, both published at version 19.0.0 in June 2025 (Release 19):
- TR 26.927 (Study on Artificial Intelligence and Machine Learning in 5G media services) covers the functional side: media-based AI/ML use cases (object recognition in image and video, video quality enhancement in streaming, crowd-sourced media capture, natural language processing on speech) and the media service architecture for AI/ML, including split-inference configurations and model delivery.
- TR 26.847 (Evaluation of Artificial Intelligence and Machine Learning in 5G media services) is the evaluation companion: testbed architectures and anchors for split inferencing and model-data transmission, evaluation metrics, and scenarios such as compressed AI/ML model transfer for automatic speech recognition and video quality enhancement in streaming. The AI/ML Evaluation Framework reference tooling is aligned with this report.
Related AI/ML studies that feed 6G
TR 22.874 (SA1, Study on traffic characteristics and performance requirements for AI/ML model transfer in 5GS) is a companion study, on the network-transport side of AI/ML rather than the media-processing side: it looks at how model-transfer traffic itself behaves on the 5G system. Both this study and TR 26.847 feed 5G-MAG's early input to 6G, where AI-native traffic management is one of the new IMT-2030 usage scenarios; see Towards 6G Media for that wider context.
Related: Towards 6G Media
Intro
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Architecture
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Features
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