Haesik Kim - Artificial Intelligence for 6G

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The cellular systems have been incrementally evolved, and the old and new network equipment co-exists for a certain period. Likewise, the 4G equipment will continuously roll out, adopt a new feature, and evolve to 5G systems. 5G systems are deployed progressively. The transition to 5G may take longer time than 4G because many different features should be included. While 5G systems are now deployed, research groups of cellular communications and networks started investigating beyond 5G systems and conceptualizing 6G systems. 6G will revolutionize the wireless communications and networks more intelligently with higher requirements than 5G systems. In the era of 6G, we need a game-changing approach. 6G systems will redefine the communications and networks depending on the required services. 6G business will not play a game but change a game. In order to support new requirements and services, a new blood technology is required. Artificial intelligence (AI) and machine learning (ML) will be one of key technologies for 6G. They are now matured technologies and improve many other research fields significantly. AI and ML make our day-to-day life easier. They pervade every aspect of our life. For example, we use mobile phone apps such as maps and navigation, facial recognition, autocorrect text, search recommendation, and so on. In addition, Chabot, social media, social media, and Internet banking are widely used. They all are based on AI and ML technologies. Thus, many experts and businessmen expect that they can dramatically improve the efficiencies of our workplaces as well as create new applications and services. AI will play a critical role in wireless communications and networks and change how we design and manage 6G communications and networks. We expect that AI makes communications and networks design and management smarter and safer. Key question is not about whether but when and how to implement AI in 6G communication systems.

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.

I am pleased to acknowledge the support of VTT Technical Research Centre of Finland and Springer and also the valuable discussion of my colleagues and experts in EU project 5G-HEART. I am grateful for the support of my family and friends.