ike applications, which can make informed predictions and decisions (e.g. managing the energy-efficient city functions). Figure 6.12 illustrates possible AI applications for measurement of QoE. In fact, bear- ing in mind that QoE is difficult to measure, unlike QoS (QoS can be measured on the basis of selected sets of KPIs, which are important to both telecom operators and cus- tomers), AI can be used to estimate the QoE for a given service by using complex anal- ysis of network QoS for different services provided via broadband and ultra-broadband access, including non-technical data such as tariff plan, age of the customer, environ- ment, end-user equipment capabilities, and other related contextual information that may influence the quality experienced by the end-users of the given service. The uses of Big Data together with AI and ML can be targeted to automation of various network functions, including automation of the network design, operation and mainte- nance, network self-optimization, and SO on. Such approaches may increase the QoE on the basis of better customization of services (e.g. use of digital assistants), or smarter use of the data (e.g. through machine learning) [34]. Further, Big Data and AI can be used for the creation of new business models, or for improvement of existing services - for 6 Services in Fixed and Mobile Ultra-Broadband Main QoS parameters (KPIs) Tariff plans analysis of estimation User device capabilities measured based on AI analysis of parameters End-to-end QoS Network performances AI - Artificial intelligence KPI - Key performance indicator QoE - Quality of experience QoS - Quality of service Figure 6.12 QoE estimation based on artificial intelligence analysis of measured QoS and user contextual information. example, important software updates for advanced smart metering, periodic reports from the devices, and network commands toward the devices. Another example could be important software update and event reports for smart trash cans. On the other side, security cameras requ