## 3 AI/ML in the Standards and Industry
### Data Analytics and AI/ML in 3 GPP
#### 3.1.1 Network Data Analytics Function (NWADF)
Operators determine the location and the type of data and KPIs to collect from the network, and specify the type of analytics the NWDAF provides to other NFs. In the B5G/6G landscape, it is envisioned that many vendors compete on the design of data collection techniques, ML model design, training, and inference, while the pipeline for communication and reporting back and forth to the NWDAF and other NFs is standardised. MNOs may also subcontract analytic services to several vendors, which increases market competition and innovation [64]. It should be noted that the NWDAF concerns analytics only in the core network; however, the separation between centralised and distributed units (CU/DU) in the RAN along with MEC have already set the stage for  
Figure 3: The NWDAF in 3 GPP Release 15 for closed-loop optimisation of network slices. data analytics in the RAN and the edge, as we will shortly discuss below [65]. Currently, the NWDAF can access 3 GPP RAN data only via the OAM entity. In this spirit, the study in [66] proposes an architecture that integrates analytics at the core network with application-level and RAN-centric analytics for E2E performance optimisation. Another point worth mentioning is that similar NFs that need to communicate often with each other might be better provided by the same vendor, at least in the early deployments of 5G core networks. Similarly, an MNO may wish to subcontract the same supplier to build the core network at a specific geographical region. Although the service-based architecture congenitally supports a multi-vendor 5G core ecosystem (known as the best-of-breed model), the actual number of vendors providing the NFs might be limited in the beginning to ensure a viable operation. Apart from the NWDAF, there is also the management data analytics function (MDAF), which is part of the operation and support system (OSS). It collects data from various repositories, the OAM system, and NFs, and provides analytics about network management and orchestration. The analytics calculated by the MDAF have a longer cycle than that collected by the NWDAF. Note that long-cycle analytics are not actionable immediately, but they can be used to identify new rules and expose hidden patterns that can eventually become a part of the NWDAF. Not surprisingly, the available studies optimising wireless networks using AI/ML and that are compatible with the service-based architecture are limited. In [67], the authors consider a system composed of five cells, three categories of subscribers, five types of end-devices, and a realistic mobility model depending on the type of the device and the time of the day. They compare three ML models residing at the NWDAF for network load prediction and anomaly detection and demonstrate that RNNs and long-short-term-memory (LSTM) outperform linear regression methods. In [66], predictive data analytics are constructed by measuring the user mobility and the received signal strength in a vehicle-to-vehicle (V2V) communication scenario. The predictions are used to perform real-time radio resource management (RRM); it is shown that the session's throughput is increased in comparison to an uncoordinated allocation of resource blocks.