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This book introduces AI techniques for wireless communications and networks and helps audiences find an optimal, sub-optimal, or trade-off solution for each communications and networks problem using AI techniques. The target audiences are senior undergraduate students, graduate students, and young researchers who have a background about fundamentals of wireless communications and networks and start studying AI and ML techniques. From this book, audiences understand how to obtain a solution under specific conditions and realize the limit of the solution. This book introduces, in a step-by-step manner, AI techniques such as unsupervised learning, supervised learning, reinforcement learning, and deep learning and explains how they are used for wireless communications and networks system. The organization of the book is as follows: In Part I, AI techniques are introduced. It will provide audiences with a mathematical background about AI algorithms. Unsupervised learning includes hierarchical clustering, partitional clustering, association rule mining, and dimensionality reduction. Supervised learning covers decision tree, K-nearest neighbouring, and support vector machine. Linear regression, gradient descent algorithms, and logistic regression are discussed. In reinforcement learning, both model-based approaches and model-free approaches are investigated. Deep learning is discussed from a perceptron to neural networks, convolutional neural networks, and recurrent neural networks. In Part II, 6G communication and network systems are designed and optimized using both wireless communications and networks techniques and AI techniques. In physical layer, data link layer, and network layers, key algorithms are selected and introduced. AI techniques are adopted in wireless communications and networks systems. We look into how AI techniques help them to improve the performance. 6G systems are now under discussion in academy, standardization body, and industry. 6G use cases, requirements, and key enabling techniques are discussed as a preliminary. In physical layer, channel model, signal detection, channel estimation, error control coding and modulation, and MIMO are explained. In data link layer, we focus on resource allocation techniques as one selected research topic. In network layer, cellular system is introduced. We focus on network traffic prediction techniques as one of key AI-enabled network layer techniques
