## 4 Experiential Networked Intelligence Architecture  
The NWDAF/MDAS-centric network automation framework in the 5G SBA focuses on the data collection and analytics information dissemination aspects of intelligent management. The other important aspect is the process of transforming collected data to information and knowledge then to intelligence for managing network operations in an \"observe-orient-decide-act\" loop. Various ML techniques may be employed at the different stages in such a loop; therefore, it is important to standardize an architecture for integrating these techniques seamlessly for achieving context-aware, ML-driven, and policy-based autonomous management. This is the main objective of the Experiential Networked Intelligence (ENI) architecture developed by ETSI [34]. The ENI architecture is designed for meeting a wide range of requirements including requirements regarding network and service planning, deployment, optimization, and provisioning; requirements related to data collection/modeling/analysis, policy management, and interaction with other systems; and non-functional requirements regarding system performance (e.g., latency, accuracy, efficiency), reusability, extensibility, etc. [35]. The ENI architecture may be applied to virtually all management aspects of future networks with numerous use cases identified by the ETSI ENI ISG in the areas of infrastructure management, network operation, service orchestration and management, and network assurance [36]. As shown in Figure 3, the ENI architecture comprises three main modules--the input processing module, the analysis module, and the output generation module. The input processing module contains the data ingestion function and the normalization function. The analysis module consists of functions for knowledge management, context-awareness, cognition management, situation-awareness, model-driven engineering, and policy man agement. The output generation module comprises the denormalization function and the output generation functions [34]. The data ingestion function is responsible for collecting data from multiple sources and processing the collected data to make them ready for being further processed and analyzed by other ENI functions. The normalization function translates the data received from the ingestion function into a form that other ENI functions can understand and use. These two functions for input processing may be combined into a single functional block for achieving more efficient data pre-processing in practical implementations of the ENI architecture. Functions in the analysis module form the core of an ENI system, which can be further split to two categories: (i) functions for knowledge management and processing, including knowledge management, context-awareness, and cognition management functions; and (ii) functions for situation-aware model-driven policy generation, including situation-awareness, model-driven engineering, and policy management functions. The knowledge management function provides a standard representation of the information about the ENI system and the systems under its management. Such a standard information representation forms a basis for leveraging various ML and data analytics techniques in the ENI architecture. The context-awareness function describes the environment in which the managed system exists or has existed, which is used to continuously update the context in which management decisions are made. The cognition management function is responsible for two key aspects of analysis: first understanding the normalized ingested data/information, as well as the context in which the data/information are produced, then evaluating the meaning of the data/information to determine if any operation/management action should be taken. The situation-awareness function allows the ENI system to be aware of the current situation of the managed system, including how the ENI analytics results are impacting the system operations.