5G/NR  -  NWDAF  

 

 

 

NWDAF

NWDAF stands for NetWork Data Analytic Function. Simply put, this functionality establishes interface and protocols to almost every important components of corenetwork and retrieve the data from them and perform analysis.

It is a component introduced in 5G networks to provide data analytics services, enabling more intelligent and automated network management. It collects, analyzes, and utilizes data from various network functions and external sources to generate insights that help optimize network performance, predict traffic patterns, enhance security, and support service assurance. By providing these insights, NWDAF enables network operators to make data-driven decisions, leading to improved efficiency, resource utilization, and a better overall user experience.

Where does it get data from ?

NWDAF obtains data from various sources within the 5G network, including other network functions like the Access and Mobility Management Function (AMF), Session Management Function (SMF), Policy Control Function (PCF), and Unified Data Management (UDM). It also collects data from network exposure functions, application functions, and other service functions that provide information on user mobility, session management, service quality, and network performance. This diverse range of data inputs allows NWDAF to perform comprehensive analytics and generate insights across multiple dimensions of the network.

The connection/interface between NWDAF and various core network components can be illustrated as follows. This illustraction was reconstructed from diagrams in 23.501-4.2.3 and tables from 23.288 - 6.2.2.1. You would notice there are some components that are not directly connected by NWDAF, but the component with the connection are connected to other components which are not directly connected to NWDAF. Therefore, we can say NWDAF is connected almost every component of core network directly and indirectly.

As shown in the diagram above, NWDAF is getting services from various core network components. The components providing the service (providing data) is called Service Producer. The list of service producers and type of the services are summarized in a table as shown below. For the details of each of the services, refer to corresponding sections in 23.502.

< 23.288-Table 6.2.2.1-1: NF Services consumed by NWDAF for data collection >

 

< 23.288-Table 6.2.2.1-2: NF Services consumed by NWDAF to determine which NF instances are serving a UE >

What kind of Analytic information you can get from NWDAF ?

NWDAF provides a variety of analytic information that can assist network operators in making informed decisions regarding network optimization, performance enhancement, and issue resolution. The analytics span multiple dimensions, including network performance, user behavior, and service quality, enabling predictive and prescriptive insights for more efficient network management.

Overall, NWDAF offers a comprehensive analytical view of the network, covering performance, user experience, security, and business aspects, enabling operators to make informed decisions for efficient network operation and service delivery.

  • Performance Optimization:
    • Network Performance: Throughput, latency, resource utilization, bottleneck identification, and QoS monitoring.
    • Service Experience: Specific service performance (e.g., video streaming, voice calls) for targeted optimization.
    • Load Analytics: Current and predicted load for efficient resource allocation and load balancing.
    • Predictive Analytics: Network state forecasting (traffic trends, capacity bottlenecks) for proactive management.
    • Failure Prediction: Likelihood of future failures and root cause analysis for faster troubleshooting.
  • User-Centric Insights:
    • User Experience: Behavior analysis, QoE assessment (session drops, mobility), and service usage trends.
    • Mobility Patterns: Analysis for handover optimization and overall network efficiency improvements.
    • QoE Predictions: Forecasting using machine learning to enable proactive resource management.
  • Security and Anomaly Detection:
    • Security Analytics: Detection of unusual network patterns or behaviors indicating security threats or network faults.
  • Network Management and Planning:
    • Network Slicing: Performance insights into network slices for resource allocation and optimization.
    • Infrastructure Forecasting: Predicting future resource needs based on historical data, aiding network expansion planning.
  • Business Intelligence:
    • Market and Customer Insights: Data-driven insights to identify trends and customer preferences.
    • Service and Monetization: Developing new services and revenue streams based on these insights.

Followings are the list/categories of the information formally specified by 3GPP.

< 23.288-Table 7.1-2: Analytics information provided by NWDAF >

Analytics Information

Request Description

Response Description

Slice Load level information

Analytics ID: load level information

Load level of a Network Slice Instance reported either as notification of crossing of a given threshold or as periodic notification (if no threshold is provided).

Observed Service experience information

Analytics ID: Service Experience

Observed Service experience statistics or predictions may be provided for a Network Slice or an Application. They may be derived from an individual UE, a group of UEs or any UE. For slice service experience, they may be derived from an Application, a set of Applications or all Applications on the Network Slice.

NF Load information

Analytics ID: NF load information

Load statistics or predictions information for specific NF(s).

Network Performance information

Analytics ID: Network Performance

Statistics or predictions on the load in an Area of Interest; in addition, statistics or predictions on the number of UEs that are located in that Area of Interest.

UE mobility information

Analytics ID: UE Mobility

Statistics or predictions on UE mobility.

UE Communication information

Analytics ID: UE Communication

Statistics or predictions on UE communication.

Expected UE behavioural parameters

Analytics ID: UE Mobility and/or UE Communication

Analytics on UE Mobility and/or UE Communication.

UE Abnormal behaviour information

Analytics ID: Abnormal behaviour

List of observed or expected exceptions, with Exception ID, Exception Level and other information, depending on the observed or expected exceptions.

User Data Congestion information

Analytics ID: User Data Congestion

Statistics or predictions on the user data congestion for transfer over the user plane, for transfer over the control plane, or for both.

QoS Sustainability

Analytics ID: QoS Sustainability

For statistics, the information on the location and the time for the QoS change and the threshold(s) that were crossed; or, for predictions, the information on the location and the time when a potential QoS change may occur and what threshold(s) may be crossed.

The table summarizes different types of analytics information that can be requested from NWDAF and the corresponding response descriptions. Each type of analytics information has a corresponding "Analytics ID" that is used in the request to specify the desired information. The response provides the requested analytics data in the form of statistics or predictions.

  • Load-related analytics:
    • Slice Load level: Load information for a specific Network Slice Instance.
    • NF Load information: Load statistics or predictions for specific Network Functions (NFs).
    • Network Performance information: Statistics or predictions on load and number of UEs in a specific area.
    • User Data Congestion information: Statistics or predictions on user data congestion for transfer over the user plane, control plane, or both.
  • User-related analytics:
    • Observed Service experience information: Statistics or predictions on Service experience for a Network Slice or an Application.
    • UE mobility information: Statistics or predictions on User Equipment (UE) mobility.
    • UE Communication information: Statistics or predictions on UE communication.
    • Expected UE behavioural parameters: Analytics on UE Mobility and/or UE Communication.
    • UE Abnormal behaviour information: Observed or expected exceptions related to UE behavior.
  • Quality of Service (QoS) analytics:
    • QoS Sustainability: Information on the location and time of QoS changes or predictions of potential QoS changes and thresholds that may be crossed.

Signaling Protocol between NWDAF and Other components

The signaling protocol used between NWDAF and other components in the 5G network is primarily based on the 5G Service-Based Architecture (SBA), which relies on RESTful APIs and HTTP/2 communication. This allows NWDAF to communicate seamlessly with various network functions like the AMF, SMF, PCF, and others.

The signaling protocol between NWDAF and other components is thus designed to be flexible, efficient, and highly interoperable, ensuring that NWDAF can effectively collect, analyze, and disseminate analytics data within the 5G ecosystem.

The table below outlines the Network Functions (NF) services provided by NWDAF. It specifies the services NWDAF offers, the corresponding operations, the operation semantics, and the example consumers of these services.

< 29.520-Table 4.1-1: Services provided by NWDAF >

Service Name

Description

Service Operations

Operation Semantics

Example Consumer(s)

Nnwdaf_EventsSubscription

(NOTE 1)

This service enables the NF service consumers to subscribe to/unsubscribe from notifications for different analytics information from the NWDAF. It also enables the transfer of subscriptions between NWDAFs.

Subscribe

Subscribe / Notify

PCF, NSSF, AMF, SMF, NEF, AF, LMF, OAM, CEF, NWDAF, DCCF

UnSubscribe

Notify

Transfer

Request /

Response

NWDAF

Nnwdaf_AnalyticsInfo

This service enables the NF service consumers to request and get specific analytics or context information related to analytics subscriptions from the NWDAF.

Request

Request / Response

PCF, NSSF, AMF, SMF, NEF, AF, LMF, OAM, NWDAF, DCCF

ContextTransfer

Request / Response

NWDAF

Nnwdaf_DataManagement

This service enables the NF service consumers to subscribe to/unsubscribe from notifications when subscribed event(s) are detected or retrieve the subscribed data from the NWDAF.

Subscribe

Subscribe / Notify

NWDAF, DCCF, MCAF

Unsubscribe

Notify

Fetch

Request / Response

NWDAF

Nnwdaf_MLModelProvision

(NOTE 2)

This service enables the NF service consumers to subscribe to/unsubscribe from notifications when a ML model matching the subscription parameters becomes available.

Subscribe

Subscribe / Notify

NWDAF

Unsubscribe

Notify

Nnwdaf_MLModelTraining

(NOTE 3)

This service enables the NF service consumers to subscribe to/unsubscribe/modify from notifications for a ML model training.

Subscribe

Subscribe / Notify

NWDAF

Unsubscribe

Notify

Nnwdaf_MLModelMonitor

This service enables the NF service consumer to subscribe/unsubscribe for ML model accuracy, provide Analytics feedback information for the analytics generated by an NWDAF and enable the NWDAF containing AnLF registers the use and monitoring capability for an ML model into the model provider NWDAF.

Subscribe

Subscribe / Notify

NWDAF

Unsubscribe

Notify

Register

Request / Respose

Deregister

Nnwdaf_RoamingData

This service enables the consumer to subscribe/unsubscribe for input data related to roaming UE(s) for NWDAF analytics.

Subscribe

Subscribe / Notify

H-RE-NWDAF, V-RE-NWDAF

Unsubscribe

Notify

Nnwdaf_RoamingAnalytics

This service enables the NF service consumers to subscribe (or modify subscriptions) to and unsubscribe from notifications for network data analytics related to roaming UE(s).

Subscribe

(NOTE 4)

Subscribe / Notify

H-RE-NWDAF, V-RE-NWDAF

Unsubscribe

Notify

NOTE 1: This service corresponds to the Nnwdaf_AnalyticsSubscription service defined in 3GPP TS 23.288 .

NOTE 2: This service implements also the Nnwdaf_MLModelInfo service as specified in 3GPP TS 23.288  by using immediate and one-time reporting requirement.

NOTE 3: This service implements also the Nnwdaf_MLModelTrainingInfo service as specified in 3GPP TS 23.288 by using immediate and one-time reporting requirement.

NOTE 4: The Nnwdaf_RoamingAnalytics_Subscribe service operation implements also the Nnwdaf_RoamingAnalytics_Request service operation specified in 3GPP TS 23.288  by using immediate and one-time reporting requirement.

Followings are brief descriptions on this table : The table details several services offered by the Network Data Analytics Function (NWDAF) within a 5G network. These services enable various Network Function (NF) service consumers to interact with NWDAF for different data analytics purposes.

  • Key Services:
    • Nnwdaf_EventsSubscription: Handles subscriptions to and notifications from NWDAF for different types of analytics information, including transferring subscriptions between NWDAFs.
    • Nnwdaf_AnalyticsInfo:  Allows consumers to request and obtain specific analytics information or context related to their subscriptions.
    • Nnwdaf_DataManagement:  Manages data subscriptions for consumers, including subscriptions, unsubscriptions, and notifications when subscribed events are detected. Also allows retrieval of subscribed data.
    • Nnwdaf_MLModelProvision:  Manages subscriptions and notifications related to the availability of machine learning (ML) models matching specified parameters.
    • Nnwdaf_MLModelTraining:  Handles subscriptions, unsubscriptions, and notifications for ML model training processes.
    • Nnwdaf_MLModelMonitor:  Manages subscriptions and notifications for ML model accuracy, allows for providing analytics feedback, and enables registering ML model usage and monitoring with the model provider NWDAF.
    • Nnwdaf_RoamingData & Nnwdaf_RoamingAnalytics:  Enable subscriptions and notifications for input data and analytics related to roaming User Equipment (UEs), respectively.
  • Common Operations:
    • Subscribe/Unsubscribe: Used to initiate or terminate subscriptions for notifications or data.
    • Notify: NWDAF sends notifications to consumers when specific events or conditions occur.
    • Request/Response: A pattern for requesting information or actions and receiving corresponding responses.
    • Transfer & ContextTransfer: Used to manage the transfer of subscriptions or analytics context between NWDAFs.
    • Fetch: Retrieves subscribed data from NWDAF.
    • Register/Deregister: Used for registering or deregistering the use and monitoring of ML models.
  • Consumers:
    • A variety of Network Functions consume these services, including PCF, NSSF, AMF, SMF, NEF, AF, LMF, OAM, CEF, NWDAF itself, DCCF, MCAF, H-RE-NWDAF, and V-RE-NWDAF.

Use Cases and Key Issues

Now assume that you have NWDAF in place in your core network, what are you going to do with it ? would there any issues with achieving your goal in terms of NWDAF process or implementations ?

I think the final answer to these questions would be up to you and everybody would have a little bit of different answers, but as initial brainstorming TR 23.791 has pretty good list of answers to the questions. For me who would not be the one that implement this functionality, just reading the titles in the document was very helpful to get the big picture of what we can do with NWDAF. Following is the blind copy of those titles from TR 23.791. If you are interested in further detail, refer to TR 23.791.

  • Use Cases
    • Use Case 1: <how to get information from AF>
    • Use Case 2: <NWDA-Assisted QoS Provisioning>
    • Use Case 3: <NWDA-Assisted Traffic Handling>
    • Use Case 4: Using NWDAF output to customize mobility management 
    • Use Case 5: <NWDA-assisted Determination of Policy> 
    • Use Case 6: <NWDAF-Assisted QoS Adjustment> 
    • Use Case 7: NWDAF assisting 5G edge computing   
    • Use Case 8: Performance improvement and supervision of mIoT terminals   
    • Use Case 9: <NWDAF-assisted load balancing/re-balancing of network functions>   
    • Use Case 10: NWDA-assisted determination of areas with oscillation of network conditions    
    • Use Case 11: Prevention of various security attacks 
    • Use Case 12: < NWDA-Assisted predictable network performance >  
    • Use Case 13: <UE driven analytics sharing>  
    • Use Case 14: How to ensure that slice SLA is guaranteed
  • Key Issues
    • Key Issue 1: Analytic Information Exposure to 5GS NF    
    • Key Issue 2: Analytic Information Exposure to AF    
    • Key Issue 3: Interactions with 5GS NFs/AFs for Data Collection  
    • Key Issue 4: Interactions with OAM for Data Collection and Data Analytics Exposure  
    • Key Issue 5: NWDAF-Assisted QoS Profile Provisioning
    • Key Issue 6: NWDAF assisting traffic routing    
    • Key Issue 7: NWDAF assisting Future Background Data Transfer    
    • Key Issue 8: performance improvement and supervision of mIoT terminals  
    • Key Issue 9: Customizing mobility management based on NWDAF output  
    • Key Issue 10: NWDAF service support to select NF instances  
    • Key Issue 11: NWDA-Assisted predictable network performance 
    • Key Issue 12: Support of Northbound Network Status Exposure 
    • Key Issue 13: UE driven analytics   
    • Key Issue 14: How to ensure that slice SLA is guaranteed

Pathways to AI/ML

As many people would guess, I think the data collected by NWDAF can be a good target for AI/ML (Artificial Intelligence / Machine Learning).  I am pretty sure that AI/ML will get involved here.. but will AI/ML be incorporated into MWDAF or be additional application (services) sitting on top of NWDAF ?  will this AI/ML be specified by 3GPP ? or will it be driven by individual company ?

As far as I know, all of these are open questions for now.

There are some 3GPP activity as of now (Dec 2021), there is some AI/ML activity in 3GPP targetted for Rel 18, but as far as I know that activity is mostly for RAN side, not for Core Network (See this note for 3GPP AI/ML).

Reference

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