Artificial Intelligence And Quantum Computing For Advanced Wireless Networks

ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING FOR ADVANCED WIRELESS NETWORKS A comprehensive presentation of the implementation of artificial intelligence and quantum computing technology in large-scale communication networks Increasingly dense and flexible wireless networks require the use of artificial intelligence (AI) for planning network deployment, optimization, and dynamic control. Machine learning algorithms are now often used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency. In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few. The authors offer readers an intuitive and accessible path from basic topics on machine learning through advanced concepts and techniques in quantum networks. Readers will benefit from: A thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines An exploration of artificial neural networks, including multilayer neural networks, training and backpropagation, FIR architecture spatial-temporal representations, quantum ML, quantum information theory, fundamentals of quantum internet, and more Discussions of explainable neural networks and XAI Examinations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology Perfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, Artificial Intelligence and Quantum Computing for Advanced Wireless Networks is also an indispensable resource for IT support staff, along with policymakers and regulators who work in technology.

This is a comprehensive description of the principles, algorithms, design technology, quantum machine learning and quantum physics in quantum computing and communications. Current work on quantum computers is motivated by the need for significantly faster and more powerful computers, and there are already in use so-called near-term quantum computers, with initial, reduced capabilities used for research and further enhancement of the technology. The outcome is distributed quantum computing which requires quantum networks to enable connections between such computers, which is a focus of this book. Moving from introductory to advanced level, this is a step-by-step approach, with numerous design examples throughout for students to work on. This is a stand-alone volume, and onward reading is available in Wireless Quantum Networks Volume 1: Intelligent Continuous Variable Technology. Topics covered in Volume 2 include: Quantum Key Distribution; Reinforcement Learning Based on Protocols; Satellite QN; Post-Quantum Cryptography; qubit Physics; Implementation Examples; and more.

This is a comprehensive description of the principles, algorithms, design technology, quantum machine learning and quantum physics in quantum computing and communications. Unifying several disciplines in the field, especially computing and communication, the book also covers computing and artificial intelligence (quantum machine learning), quantum cyber security, quantum circuit design, and relative quantum physics phenomena. Based on the latest results in the field, enabling the reader/researcher to understand these interrelations of a complex system in practice, the book moves from introductory to advanced level, step by step, providing a self-contained text. Undergraduate and postgraduate students will find several DESIGN EXAMPLES to replace the classical concept of using “problems and solutions” addendums at the end of the chapters/book. This enables offering more sophisticated assignments for the teamwork of the students. This is a stand-alone volume, and onward reading is available in Volume 2 Quantum Vs Post Quantum Security/Algorithms and Design Technology. Topics covered in Volume 1 include: Visible Light Communication; Terahertz Communications; Optical Wireless Communications; Quantum Key Distribution over THz band; Deep Neural Networks; Quantum Network Routing and Technology Design; Quantum Machine Learning; and more.

The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand? Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks.

Since the launch of Second-Generation Networks (2G), planning for each future mobile service was initiated many years before its commercial launch. In 2019, 5G Networks begun to be deployed commercially after almost ten years of planning. Similarly, the race for the 6G wireless networks that will be operational in 2030 has already started. To fulfill its potential in the upcoming decade, 6G will undoubtedly require an architectural orchestration based on the amalgamation of existing solutions and innovative technologies. The book will begin by evaluating the state of the art of all current mobile generations' while looking into their core building blocks. 6G implementation will require fundamental support from Artificial Intelligence (AI) and Machine Learning on the network's edge and core, including a new Radio Frequency (RF) spectrum. The 6G use cases will require advanced techniques for enabling the future wireless network to be human-centric, ensuring enhanced quality of experience (QoE) for most of its applications. The concept of Human Bond Communication Beyond 2050 (Knowledge Home) and Communication, Navigation, Sensing, and Services (CONASENSE) will also profit from future wireless communication. Terahertz domains will exploit the ultra-Massive Multiple Input Multiple Output Antennas (UM-MIMO) technologies to support Terabits' data throughputs.Moreover, optical wireless communications (OWC) will also come into play to support indoor and outdoor high-data rates. Further expansion of 6G core entities will support the novel concept of Society 5.0. Quantum computing processing and communications is also likely to be added into the 6G ecosystem with security managed by blockchain orchestration for a robust network.

Quantum computing is a field in which advanced technologies like quantum communication, artificial intelligence and machine learning can be used to secure and speed up connectivity using quantum computers, quantum drones or quantum satellites. This book serve as a foundation for researchers and scientists in this field. Future technologies, such as quantum drone delivery systems, quicker internet and climate change mitigation, will need quantum information processing and quantum computation. This book deeply explores the importance of quantum computing in real-time applications. It may be used as a reference book for students in higher education, including undergraduate and graduate students, as well as researchers. Key features: Provides a clear insight into the Internet of Drones for academicians, postdoc fellows, research scholars, graduate and postgraduate students, industry fellows and software engineers Useful to professionals who seek information about the Internet of Drones, including experts in quantum computing and physics and post-quantum cryptography, as well as data scientists and data analysts Covers quantum computing and security for Unmanned Aerial Vehicles (UAV) or drones which are widely useful for applications such as military, government, and non-government systems Explores futuristic aspects of the Intenet of Drones to improve everyday living for ordinary people

Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.