5G technologies are expected to enable new verticals, services, and business models. Recently, the use of artificial intelligence (AI) and data analytics is shown to provide massive advantages in terms of reducing network complexity and enhancing its performance. In this chapter, we provide an overview of the recent studies on AI-assisted solutions in 5G wireless networks, followed by three case studies of our original work, including a Q-learning-assisted cell selection mechanism, an AI engine that enables intelligent 5G fronthaul slicing, and a beam management protocol for a multiple radio access technology (RAT) coexistence via learning. Realizing the vital role of data and data analytics in enabling AI for wireless networks in practice, we review data analytics in the current literature and discuss how data analytics and AI enable the applications in 5G networks. The recent industry and standardization activities of using AI in 5G networks are summarized. Finally, we give our insights on the research challenges and open questions.

Chapter Contents:

• 7.1 Introduction
• 7.2 Case studies of AI in 5G wireless networks
• 7.2.1 Case study 1: AI for cell selection
• 7.2.2 Case study 2: AI for 5G fronthaul
• 7.2.2.1 Fronthaul load prediction
• 7.2.2.2 Dynamic functional split optimization
• 7.2.2.3 Fronthaul inter-slice load balancing
• 7.2.3 Case study 3: AI for coexistence of multiple radio access technologies
• 7.3 Data analytics in 5G
• 7.4 Industry and standard activities
• 7.4.1 Open standards required
• 7.4.2 Achievements and activates of standardization
• 7.5 Challenges and open questions
• 7.5.1 Big data or small data
• 7.5.2 Centralized or distributed learning
• 7.6 Conclusions
• References

Inspec keywords: radio access networks; data analysis; telecommunication traffic; 5G mobile communication; learning (artificial intelligence); mobility management (mobile radio); telecommunication network reliability

Other keywords: data analytics; AI engine; artificial intelligence; wireless networks; intelligent 5G fronthaul slicing; network complexity; Q-learning-assisted cell selection mechanism; multiple radio access technology coexistence; AI-assisted solutions; beam management protocol

Subjects: Radio access systems; Data handling techniques; Mobile radio systems; Knowledge engineering techniques; Reliability