Network Data Analytics Function (NWDAF): 5G Network Function

The Network Data Analytics Function (NWDAF) is a 5G network function that collects data from various 5G Core network functions, application functions, as well as operations, administration, and management (OAM) systems, and operational support systems. The NWDAF then performs analytics on this data to provide insights into the performance and health of the network.

The NWDAF can be used for a variety of purposes, including:

??????????????? ?????????????????????? ????????????????????:?The NWDAF can be used to monitor the performance of the network, such as the latency, throughput, and availability of various network resources. This information can be used to identify and troubleshoot performance problems.

??????????????? ???????????????? ????????????????:?The NWDAF can be used to analyze network traffic to identify potential security threats. This information can be used to improve the security of the network.

????????????????? ???????????????????? ????????????????????????:?The NWDAF can be used to analyze customer data to identify trends and patterns that can be used to improve the customer experience. For example, the NWDAF can be used to identify areas where the network is not performing well for certain types of customers or applications.

?????????????-???????? ????????????????????:?The NWDAF can be used to automate network operations. For example, the NWDAF can be used to automatically generate alerts when there are performance problems or security threats.

The NWDAF is a key component of the 5G network. It provides a centralized platform for collecting and analyzing network data. This information can be used to improve the performance, security, and customer experience of the network.

Functionalities

????Support data collection from NFs and AFs.

????Support data collection from OAM.

????NWDAF service registration and metadata exposure to NFs and AFs.

????Support analytics information provisioning to NFs and AFs.

????Support Machine Learning (ML) model training and provisioning to NWDAFs (containing Analytics logical function).

As depicted in Figure?1.0, the 5G System architecture allows NWDAF to collect data from any 5GC NF. The NWDAF belongs to the same PLMN as the 5GC NF that provides the data.

As depicted in Figure 1.1, the Ndccf interface is defined for the NWDAF to support subscription request(s) for data delivery from a DCCF, to cancel subscription to data delivery and to request a specific report of data.

As depicted in Figure?1.2, the 5G System architecture allows any 5GC NF to request network analytics information from NWDAF containing Analytics logical function (AnLF). The NWDAF belongs to the same PLMN as the 5GC NF that consumes the analytics information.

As depicted in Figure 1.3, the Ndccf interface is defined for any NF to support subscription request(s) to network analytics, to cancel subscription for network analytics and to request a specific report of network analytics. If the analytics is not already being collected, the DCCF requests the analytics from the NWDAF using Nnwdaf services. The DCCF may collect the analytics and deliver it to the NF, or the DCCF may rely on a messaging framework to collect analytics and deliver it to the NF.

As depicted in Figure?1.4, the 5G System architecture allows NWDAF containing Analytics logical function (AnLF) to use trained ML model provisioning services from another NWDAF containing Model Training logical function (MTLF).

???Analytics logical function (AnLF): A logical function in NWDAF, which performs inference, derives analytics information (i.e. derives statistics and/or predictions based on Analytics Consumer request) and exposes analytics service i.e. Nnwdaf_AnalyticsSubscription or Nnwdaf_AnalyticsInfo.

???Model Training logical function (MTLF): A logical function in NWDAF, which trains Machine Learning (ML) models and exposes new training services (e.g. providing trained ML model)?

As depicted in Figure?1.5, the 5G System architecture allows ADRF to store and retrieve the collected data and analytics.

???ADRF exposes the Nadrf service for storage and retrieval of data by other 5GC NFs (e.g. NWDAF) which access the data using Nadrf services.

???Based on the NF request or configuration on the DCCF, the DCCF may determine the ADRF and interact directly or indirectly with the ADRF to request or store data.

??The ADRF stores data received in a Nadrf_DataManagement_Storage Request sent directly from an NF, or data received in an Ndccf_DataManagement_Notify / Nmfaf_3caDataManagement_Notify or Nnwdaf_DataManagement_Notify from the DCCF, MFAF or from the NWDAF.

??The ADRF checks if the Data Consumer is authorized to access ADRF services.

As depicted in Figure?1.6, based on operator's policy and local regulations (e.g. privacy), data or analytics may be exchanged between PLMNs (i.e. HPLMN and VPLMN). In a PLMN, an NWDAF is used as entry point to exchange analytics and to collect input data for analytics with other PLMNs. The NWDAF with roaming entry capability is called Roaming Entry NWDAF (RE-NWDAF).

Federated Learning (FL) among multiple NWDAFs

Federated learning among multiple NWDAFs is a machine learning technique in core network that trains an ML Model across multiple decentralized entities holding local data set, without exchanging/sharing local data set. This approach stands in contrast to traditional centralized machine learning techniques where all the local datasets are uploaded to one server, thus allowing to address critical issues such as data privacy, data security, data access rights.

For Federated Learning supported by multiple NWDAFs containing MTLF, there is one NWDAF containing MTLF acting as FL server (called FL server NWDAF for short) and multiple NWDAFs containing MTLF acting as FL client (called FL client NWDAF for short), the main functionality includes:

FL Server NWDAF:

???Discovers and selects FL client NWDAFs to participant in an FL procedure

???Requests FL client NWDAFs to do local model training and to report local model information.

???Generates global ML model by aggregating local model information from FL client NWDAFs.

? Sends the global ML model back to FL client NWDAFs and repeats training iteration if needed.

FL Client NWDAF:

???Locally trains ML model that tasked by the FL server NWDAF with the available local data set, which includes the data that is not allowed to share with others due to e.g. data privacy, data security, data access rights.

? Reports the trained local ML model information to the FL server NWDAF.

? Receives the global ML model feedback from FL server NWDAF and repeats training iteration if needed.

???????????????? ???? ?????????? ?????????? ???? ???? ????????????????

?? Improved network performance:?The NWDAF can help to identify and troubleshoot performance problems. This can help to improve the performance of the network for all users.

?? Enhanced security:?The NWDAF can help to identify and mitigate security threats. This can help to protect the network from unauthorized access and malicious attacks.

?? Optimized customer experience:?The NWDAF can help to improve the customer experience by identifying trends and patterns that can be used to improve the network for specific groups of users.

?? Automated network operations:?The NWDAF can help to automate network operations. This can free up network operators to focus on other tasks, such as improving the performance of the network.

Overall, the NWDAF is a key component of the 5G network. It provides a number of benefits, including improved network performance, enhanced security, optimized customer experience, and automated network operations.

Refer 3gpp.org || TS 23.501, TS 23.288