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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.
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.
The First Edition of the book ''An Introduction to Machine Learning'' combines theory and practice, explaining important methods such as classical linear and logistic regression, deep learning, and neural network with a detailed explanation, all variants of models, suitable examples, and Python code snippets.
RESOURCE MANAGEMENT IN ADVANCED WIRELESS NETWORKS Written and edited by a team of experts in the field, this exciting new volume provides a comprehensive exploration of cutting-edge technologies and trends in managing resources in advanced wireless networks. This groundbreaking new volume from Wiley-Scrivener discusses the challenges that are emerging while managing the resources in various wireless networking technologies. Initially, the evolution of wireless networking technologies is presented, focusing on the advantages of improving data rates and data reliability. The book then goes through the various architecture designs based on the network paradigms, along with the evolution of networks based on the trends in the telecommunication industry. Various salient features are highlighted in managing resources, and the role of routing strategies is addressed with regard to real-time applications. Covering resource management in wireless networks, various industries are covered, such as healthcare and financial services, but the ideas are useful across many industries. Whether for the veteran engineer, industry professional, or student, this is a must- have for any library.
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.
EMERGING COMPUTING PARADIGMS A holistic overview of major new computing paradigms of the 21st Century In Emerging Computing Paradigms: Principles, Advances and Applications, international scholars offer a compendium of essential knowledge on new promising computing paradigms. The book examines the characteristics and features of emerging computing technologies and provides insight into recent technological developments and their potential real-world applications that promise to shape the future. This book is a useful resource for all those who wish to quickly grasp new concepts of, and insights on, emerging computer paradigms and pursue further research or innovate new novel applications harnessing these concepts. Key Features Presents a comprehensive coverage of new technologies that have the potential to shape the future of our world—quantum computing, computational intelligence, advanced wireless networks and blockchain technology Revisits mainstream ideas now being widely adopted, such as cloud computing, the Internet of Things (IoT) and cybersecurity Offers recommendations and practical insights to assist the readers in the application of these technologies Aimed at IT professionals, educators, researchers, and students, Emerging Computing Paradigms: Principles, Advances and Applications is a comprehensive resource to get ahead of the curve in examining and exploiting emerging new concepts and technologies. Business executives will also find the book valuable and gain an advantage over competitors in harnessing the concepts examined therein.
The integration of artificial intelligence (AI), quantum computing, and semiconductor technology offers improved innovation to redefine computational power and capabilities. As AI drives advances in machine learning and data processing, quantum computing revolutionizes problem-solving with its ability to handle complex calculations at improved speeds. Advancements in semiconductor technology push the limits of processing efficiency and miniaturization. Continued exploration on this convergence may accelerate breakthroughs in various fields such as cryptography, material science, and healthcare. Integration of AI, Quantum Computing, and Semiconductor Technology explores the intersection of artificial intelligence (AI) and semiconductor technology within the context of quantum computing. It offers a comprehensive analysis of the current advancements, challenges, and potential applications resulting from this convergence. This book covers topics such as cyber security, healthcare monitoring, and machine learning, and is a useful resource for computer engineers, energy scientists, business owners, healthcare administrators, environmental scientists, academicians, and researchers.
- provides some fundamental concepts related to 5G networks and the 5G NR signal processing. A review of AI and state of the art machine learning techniques is also given. - deals with the 5G/6G and AI enabled applications such as AR/VR, autonomous vehicles, mobile multimedia services, context aware communications, Industrial IoT and security. -elaborates on how AI techniques can enhance network and traffic management in 5G/6G networks. These include AI based mobility management, routing, scheduling, network performance optimization and even energy efficiency. -discusses the application of AI to 5G/6G NR signal processing and also the air interface. AI and deep learning techniques for channel coding, automatic modulation detection, channel estimation and equalization as well as spectrum management are presented with a view to highlight the benefits of using AI as compared to traditional techniques.
This book provides a tutorial on quantum communication networks. The authors discuss current paradigm shifts in communication networks that are needed to add computing and storage to the simple transport ideas of prevailing networks. They show how these ‘softwarized’ solutions break new grounds to reduce latency and increase resilience. The authors discuss how even though these solutions have inherent problems due to introduced computing latency and energy consumption, the problems can be solved by hybrid classical-quantum communication networks. The book brings together quantum networking, quantum information theory, quantum computing, and quantum simulation.