## 3 AI/ML in the Standards and Industry
### Data Analytics and AI/ML in 3 GPP
#### 3.1.2 European Telecommunications Standards Institute (ETSI)  
Within 3 GPP, the ETSI has already established several committees working on AI/ML-based operation for 5G/B5G wireless networks to enhance their automation and autonomy levels [77]. We discuss below the activities for two of these committees; the former is working towards the experiential networking intelligence (ENI) system, and the latter is producing the zero-touch network and service management (ZSM) architecture. ETSI ENI  
In traditional communication networks, network operation, optimisation and management are costly and time-consuming tasks exacerbated by vendor inter-operability issues and the recent advent of personalised services. The ENI initiative, founded in 2017, designs an AI-based cognitive network management system that provides feedback, recommendations, or commands to another system, e.g., to an MNO, an orchestrator, a user, or an application, aiming at improving the other system's performance and increasing its autonomy level in a cost effective manner [78]. The so-called assisted system may or may not have AI/ML functional modules, and most importantly, it does not need to change its functionality to receive help from the ENI system, as illustrated in Figure 5. To accelerate its adoption, the ENI system comes with an API broker that acts as a gateway and translates between the APIs of the two systems (assisted and ENI). The ENI system comprises six functional blocks, including situation-awareness and context-awareness functions (which enable it to dynamically adapt to changes in the environment), the assisted system's management objectives, business goals, and user needs. Essentially, the ENI and the assisted systems together create a closed loop so that the ENI system always receives updated data from the assisted system, and in turn, provides AI/ML-driven recommendations for network management, orchestration, etc. The ENI committee has identified several use cases where AI seems to have a high potential in improving network operation and management, and grouped them under five main categories, specifically, infrastructure management, network assurance, network operations, service orchestration and management, and network security [80]. Example representative use cases for each category are energy optimisation, network fault prediction, radio coverage and capacity optimisation, intelligent caching based on content popularity predictions, and policy-based network slicing for IoT security, respectively. For instance, the ENI system can interact through standardised interfaces (reference points) with an MNO, collect data and learn about the MNO's operation and performance, and finally, suggest methods to optimise the MNO orchestration of network slices. The recommendations can be dynamically updated as the ENI system collects more data, the environment changes or the goals of the MNO vary in time. Subsequently, the selected use cases by the ENI committee are associated with the requirements that the ENI system must fulfil [81]. To give an example, a functional requirement shall specify how to perform data collection, analysis, and learning, or how to inter-work with other systems, including the assisted system, while a network requirement shall determine how to allocate network resources to the virtual NFs of the assisted system. In a nutshell, the ENI system is a versatile, AI/ML-based system with generic architecture that can support various decision-making processes and systems to achieve their goals, as specified in the ENI use cases. There are ongoing ENI system demonstrations (proofs-of-concept) [82], while the latest positioning white paper produced by the ENI committee is available in [83]. The ZSM committee is another cross-industry initiative, launched by ETSI in 2017, whose aim is to design a reference architecture for future communication networks, that are solely managed by AI/ML without any human intervention [84].