## 3 AI/ML in the Standards and Industry
### International Telecommunication Union (ITU)
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Categories** & **Use Cases** \\ \hline Network slice and other network service-related use cases. & * Cognitive heterogeneous networks and ML-based self-optimised networks. * Radio resource management (RRM) for network slicing. * E2E network operation automation. \\ \multicolumn{2}{p{142.3 pt}}{} \\ Service design & * Network resource adaptation \\ \multicolumn{2}{p{142.3 pt}}{} \\ Logical network design and deployment & * Fault detection and recovery \\ \multicolumn{2}{p{142.3 pt}}{} \\ Application-specific network slicing through in-network ML. * Smart traffic mirror—an ML-assisted network service. * ML-based E2E network slicing for 5G. * ML-based utility maximisation of sliced backhauls. * Energy efficient trusted multi-tenancy in IMT-2020 cross-haul. * Network slice SLA assurance based on ML. * Service management for smart cities. * Automated testing of services. \\ \hline User-plane related use cases. & * Traffic classification. * Long-term traffic forecasting. * Emergency services based on ML. \\ \hline Application related use cases. & * Access network assisted transmission control protocol window optimisation. * Retention and storage intelligence function. * Data-driven architecture for ML at the edge. \\ \hline Signaling or management related use cases. & * ML mobility pattern prediction. * Load balance and cell splitting/merging. * ML-based QoE optimisation. * ML-based network management for Industry 4.0. * ML-based correlations between transport KPIs and radio KPIs. * ML-based E2E network management. * ML-aided channel modelling and channel prediction. * ML-based link adaptation optimisation. \\ \hline Security-related use cases & * Combatting use of counterfeit ICT devices—ML-assisted network service. * ML-based identification of illegal exchanges using SIM boxes. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Identified use cases for ML in future networks by FG-ML5G [89]. the underlay network [92]. Therefore, the sandbox enables the ML pipeline to dynamically adapt to changes in the underlay network or the environment. The selected ML models are finally stored in a repository known as the ML marketplace, which is managed by an orchestrator and is responsible for selecting the best ML model according to the needs of the subject use case. For a high-level overview of the interactions among the architectural components recommended by the FG-ML5G, see Figure 7.  
During its lifetime, the FG-ML5G had a strong interaction with MNOs, with the ETSI ENI committee for creating a framework for the evaluation of intelligence levels [93], with the ETSI ZSM committee for designing the ML pipeline architecture, etc. Additionally, the FG encouraged the use of open-source frameworks and AI platforms, such as the Linux foundation for AI Acumos [94]. To engage the research community, the FG organised a global AI challenge between March and December 2020, where research teams competed to solve real telecommunication industry problems using AI/ML, e.g., network topology optimisation, and PHY channel estimation [95]. The ITU-T SG13 focus group on autonomous networks (FG-AN) was established in December 2020 and is the natural continuation of FG-ML5G [96].