## 3 AI/ML in the Standards and Industry
### Data Analytics and AI/ML in 3 GPP
#### 3.1.1 Network Data Analytics Function (NWADF)  
The NWDAF resides at the core network and utilises a bus architecture known as the service-based interface to communicate with other network functions (NFs). In the context of IMT-2020, a NF processes a certain network node functionality, e.g., session or mobility management, and has well-defined interfaces. In the cloud-native architecture, the virtualised NFs are broken down into smaller software units known as microservices, which are easier to manage and upgrade via open application programming interfaces (APIs) than large software components. Apart from NFs, the NWDAF can also provide analytics to application functions and the operation administration and maintenance (OAM) system. The interaction between the NWDAF and other NFs follows the consumer-producer model. The NFs may subscribe to the NWDAF and request specific analytics (known as use cases or analytic events), which are given a unique identification number (ID) so that a NF can request a service from the NWDAF via its ID. The requests can be either periodic through a subscription to the NWDAF or ad hoc, i.e., one-off reporting request from the NWDAF. NWDAF in Releases 15 and 16  
In 3 GPP Release 15, the consumers of the NWDAF are the policy control function (PCF) and the network slice selection function (NSSF); see Figure 3. The NWDAF receivesload information from the NSSF and provides analytics that can be used to determine the optimal way to create or select the network slice(s) serving a UE or a group of UEs. The PCF uses NWDAF analytics to perform traffic steering to alleviate congestion at the core network and optimise the assignment of network resources [62]. In a nutshell, the NWDAF in Release 15 equips MNOs with data analytics to optimise and smartly manage several network slices, potentially running on the same physical infrastructure and instructing every few minutes or so the number of resources to allocate to each slice. This functionality is crucial to meet SLAs in a landscape with several offered services having diverged and/or conflicting latency, reliability, and data rate requirements. It is well understood that creating fine-grained slices and automating the allocation of network resources through AI/ML is imperative to ensure QoE per customer and service. Therefore, the NWDAF, where the AI/ML models reside, is a key component to achieve this goal. The alternative approach using large and relatively static slices will perform poorly as new services with diverse and stringent requirements appear in the market. In 3 GPP Release 16, the NWDAF is allocated and dedicated to study and work items; it goes beyond closed-loop optimisation of network slices and interacts with many more NFs at the core network, e.g., access and mobility management (AMF) and session management (SMF) (see [63]). In principle, the NWDAF can exchange data with any NF in the core network, and correlate data received from multiple NFs to expose hidden patterns. NFs can also communicate with each other through the NWDAF. To give an example, the AMF supports (un)subscription to the notification of analytics information for SMF load information from NWDAF to improve SMF selection. Therefore, the NWDAF is both a producer and consumer in the service-based architecture. Overall, in Release 16, the NFs may provide local information to the NWDAF and request data analysis about various standardised events (using the unique ID of the event), including customised mobility management, mobility prediction, load balancing, QoS sustainability, service experience, and network performance; see [61] for more details on standardised analytic events. While the communication interfaces (i.e., the APIs) between the NWDAF and other NFs are standardised by 3 GPP, the implementation of specific AI/ML algorithms for big data analytics and closed-loop optimisation and automation are open to the needs of operators and vendors.