The connected world paradigm effectuated through the proliferation of mobile devices, Internet of Things (IoT), and the metaverse will offer novel services in the coming years that need anytime, anywhere, high-speed access. The success of this paradigm will highly depend on the ability of the devices to always obtain the optimal network connectivity for an application and on the seamless mobility of the devices. This book will discuss 6G concepts and architectures to support next-generation applications such as IoT, multiband devices, and high-speed mobile applications. IoT applications put forth significant challenges on the network in terms of spectrum utilization, latency, energy efficiency, large number of users, and supporting different application characteristics in terms of reliability, data rate, and latency.While the 5G network developmentwas motivated by the need for larger bandwidth and higher quality of service (QoS), 6G considerations are supporting many users with a wide application requirement, lowering network operating cost, and enhanced network flexibility. Network generations beyond 5G are expected to accommodate massive number of devices with the proliferation of connected devices concept in connected cars, industrial automation, medical devices, and consumer devices. This book will address the fundamental design consideration for 6G networks and beyond. There are many technical challenges that need to be explored in the next generation of networks, such as increased spectrum utilization, lower latency, higher data rates, accommodating more users, heterogeneous wireless connectivity, distributed algorithms, and device-centric connectivity due to diversified mobile environments and IoT application characteristics. Since 6G is a multidisciplinary topic, this book will primarily focus on aspects of device characteristics, wireless heterogeneity, traffic engineering, device-centric connectivity, and smartness of application.

This book is the world’s first book on 6G Mobile Wireless Networks that aims to provide a comprehensive understanding of key drivers, use cases, research requirements, challenges and open issues that are expected to drive 6G research. In this book, we have invited world-renowned experts from industry and academia to share their thoughts on different aspects of 6G research. Specifically, this book covers the following topics: 6G Use Cases, Requirements, Metrics and Enabling Technologies, PHY Technologies for 6G Wireless, Reconfigurable Intelligent Surface for 6G Wireless Networks, Millimeter-wave and Terahertz Spectrum for 6G Wireless, Challenges in Transport Layer for Tbit/s Communications, High-capacity Backhaul Connectivity for 6G Wireless, Cloud Native Approach for 6G Wireless Networks, Machine Type Communications in 6G, Edge Intelligence and Pervasive AI in 6G, Blockchain: Foundations and Role in 6G, Role of Open-source Platforms in 6G, and Quantum Computing and 6G Wireless. The overarching aim of this book is to explore the evolution from current 5G networks towards the future 6G networks from a service, air interface and network perspective, thereby laying out a vision for 6G networks. This book not only discusses the potential 6G use cases, requirements, metrics and enabling technologies, but also discusses the emerging technologies and topics such as 6G PHY technologies, reconfigurable intelligent surface, millimeter-wave and THz communications, visible light communications, transport layer for Tbit/s communications, high-capacity backhaul connectivity, cloud native approach, machine-type communications, edge intelligence and pervasive AI, network security and blockchain, and the role of open-source platform in 6G. This book provides a systematic treatment of the state-of-the-art in these emerging topics and their role in supporting a wide variety of verticals in the future. As such, it provides a comprehensive overview of the expected applications of 6G with a detailed discussion of their requirements and possible enabling technologies. This book also outlines the possible challenges and research directions to facilitate the future research and development of 6G mobile wireless networks.

To provide ubiquitous and various services, 6G networks tend to be more comprehensive and multidimensional by integrating current terrestrial networks with space-/air-based information networks and marine information networks; then, heterogeneous network resources, as well as different types of users and data, will be also integrated. Driven by the exponentially growing demands of multimedia data traffic and computation-heavy applications, 6G heterogenous networks are expected to achieve a high QoS with ultra-reliability and low latency. In response, resource allocation has been considered an important factor that can improve 6G performance directly by configuring heterogeneous communication, computing and caching resources effectively and efficiently. The book addresses a range of technical issues in cooperative resource allocation and information sharing for the future 6G heterogenous networks, from the terrestrial ultra-dense networks and space-based networks to the integrated satellite-terrestrial networks, as well as introducing the effects of cooperative behavior among mobile users on increasing capacity, trustworthiness and privacy. For the cooperative transmission in heterogeneous networks, the authors commence with the traffic offloading problems in terrestrial ultra-dense networks, and the cognitive and cooperative mechanisms in heterogeneous space-based networks, the stability analysis of which is also provided. Moreover, for the cooperative transmission in integrated satellite-terrestrial networks, the authors present a pair of dynamic and adaptive resource allocation strategies for traffic offloading, cooperative beamforming and traffic prediction based cooperative transmission. Later, the authors discuss the cooperative computation and caching resource allocation in heterogeneous networks, with the highlight of providing our current studies on the game theory, auction theory and deep reinforcement learning based approaches. Meanwhile, the authors introduce the cooperative resource and information sharing among users, in which capacity oriented-, trustworthiness oriented-, and privacy oriented cooperative mechanisms are investigated. Finally, the conclusion is drawn.

This book highlights future research directions and latent solutions by integrating AI and Blockchain 6G networks, comprising computation efficiency, algorithms robustness, hardware development and energy management. This book brings together leading researchers in Academia and industry from diverse backgrounds to deliver to the technical community an outline of emerging technologies, advanced architectures, challenges, open issues and future directions of 6G networks. This book is written for researchers, professionals and students to learn about the integration of technologies such as AI and Blockchain into 6G network and communications. This book addresses the topics such as consensus protocol, architecture, intelligent dynamic resource management, security and privacy in 6G to integrate AI and Blockchain and new real-time application with further research opportunities.

ARTIFICIAL INTELLIGENT TECHNIQUES FOR WIRELESS COMMUNICATION AND NETWORKING The 20 chapters address AI principles and techniques used in wireless communication and networking and outline their benefit, function, and future role in the field. Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments. Audience Researchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies.

To overcome the constraints of 5G for supporting new challenges, 6G wireless systems must be developed with new and attractive features. These systems are expected to increase performance and maximize quality of service several folds more than 5G along with other exciting features. However, 6G is still in its infancy and must be explored. The Handbook of Research on Design, Deployment, Automation, and Testing Strategies for 6G Mobile Core Network discusses the technological feats used in the new 6G wireless systems. It discusses the design, automation, and uses for industry as well as testing strategies. Covering topics such as 6G architecture, smart healthcare, and wireless communication, this major reference work is an excellent resource for computer scientists, engineers, students and professors in higher education, researchers, and academicians.

This book is a collection of papers from international experts presented at International Conference on NextGen Electronic Technologies (ICNETS2-2016). ICNETS2 encompassed six symposia covering all aspects of electronics and communications domains, including relevant nano/micro materials and devices. Presenting recent research on wireless communication networks and Internet of Things, the book will prove useful to researchers, professionals and students working in the core areas of electronics and their applications, especially in signal processing, embedded systems and networking.

DESCRIPTION Our book is divided into several useful concepts and techniques of machine learning. This book serves as a valuable resource for individuals seeking to deepen their understanding of advanced topics in this field. Learn about various learning algorithms, including supervised, unsupervised, and reinforcement learning, and their mathematical foundations. Discover the significance of feature engineering and selection for enhancing model performance. Understand model evaluation metrics like accuracy, precision, recall, and F1-score, along with techniques like cross-validation and grid search for model selection. Explore ensemble learning methods along with deep learning, unsupervised learning, time series analysis, and reinforcement learning techniques. Lastly, uncover real-world applications of the machine and deep learning algorithms. After reading this book, readers will gain a comprehensive understanding of machine learning fundamentals and advanced techniques. With this knowledge, readers will be equipped to tackle real-world problems, make informed decisions, and develop innovative solutions using machine and deep learning algorithms. KEY FEATURES ● Basic understanding of machine learning algorithms via MATLAB, R, and Python. ● Inclusion of examples related to real-world problems, case studies, and questions related to futuristic technologies. ● Adding futuristic technologies related to machine learning and deep learning. WHAT YOU WILL LEARN ● Ability to tackle complex machine learning problems. ● Understanding of foundations, algorithms, ethical issues, and how to implement each learning algorithm for their own use/ with their data. ● Efficient data analysis for real-time data will be understood by researchers/ students. ● Using data analysis in near future topics and cutting-edge technologies. WHO THIS BOOK IS FOR This book is ideal for students, professors, and researchers. It equips industry experts and academics with the technical know-how and practical implementations of machine learning algorithms. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Statistical Analysis 3. Linear Regression 4. Logistic Regression 5. Decision Trees 6. Random Forest 7. Rule-Based Classifiers 8. Naïve Bayesian Classifier 9. K-Nearest Neighbors Classifiers 10. Support Vector Machine 11. K-Means Clustering 12. Dimensionality Reduction 13. Association Rules Mining and FP Growth 14. Reinforcement Learning 15. Applications of ML Algorithms 16. Applications of Deep Learning 17. Advance Topics and Future Directions