Tutorial Machine Learning for AIDriven Wireless Networks: Challenges and Opportunities The goal of this tutorial is to provide one of the first holistic tutorials on the topic of machine learning for wireless network design. In particular, we will first provide a comprehensive treatment of the fundamentals of machine learning and artificial neural networks, which are one of the most important pillars of machine learning. After providing a substantial introduction to the basics of machine learning, we introduce a classification of the various types of neural networks that include feedforward neural networks, recurrent neural networks, spiking neural networks, and deep neural networks. For each type, we provide an introduction on their basic components, their training processes, and their use cases with specific example neural networks. Then, we overview a broad range of wireless applications that can make use of neural network designs. This range of applications includes spectrum management, multiple radio access technology cellular networks, wireless virtual reality, mobile edge computing and caching, dronebased communications, the Internet of Things, and vehicular networks. For each application, we first outline the main rationale for applying machine learning while pinpointing illustrative scenarios. Then, we overview the challenges and opportunities brought forward by the use of neural networks in the specific wireless application. We complement this overview with a detailed example drawn from the stateoftheart. Finally, we conclude by shedding light on the potential future works within each specific area and within the overall area of AI for wireless networks.

Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges. The fifth generation (5G) wireless communication networks are currently being deployed, and beyond 5G (B5G) networks are expected to be developed over the next decade. Artificial intelligence (AI) technologies and, in particular, machine learning (ML) have the potential to efficiently solve the unstructured and seemingly intractable problems by involving large amounts of data that need to be dealt with in B5G. This article studies how AI and ML can be leveraged for the design and operation of B5G networks. We first provide a comprehensive survey of recent advances and future challenges that result from bringing AIML technologies into B5G wireless networks. Our survey touches different aspects of wireless network design and optimization, including channel measurements, modeling, and estimation, physicallayer research, and network management and optimization. Then, ML algorithms and applications to B5G networks are reviewed, followed by an overview of standard developments of applying AIML algorithms to B5G networks. We conclude this study by the future challenges on applying AIML to B5G networks.