{"product_id":"artificial-intelligence-and-quantum-computing-for-advanced-wireless-networks-isbn-9781119790297","title":"Artificial Intelligence and Quantum Computing for Advanced Wireless Networks","description":"\u003cb\u003eARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING FOR ADVANCED WIRELESS NETWORKS\u003c\/b\u003e \u003cp\u003e\u003cb\u003eA comprehensive presentation\u003c\/b\u003e \u003cb\u003eof the implementation \u003c\/b\u003e\u003cb\u003eof artificial intelligence and quantum computing technology in large-scale communication networks\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIncreasingly 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.\u003c\/p\u003e \u003cp\u003eIn \u003ci\u003eArtificial Intelligence and Quantum Computing for Advanced Wireless Networks\u003c\/i\u003e, 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.\u003c\/p\u003e \u003cp\u003eThe 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines\u003c\/li\u003e \u003cli\u003eAn 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\u003c\/li\u003e \u003cli\u003eDiscussions of explainable neural networks and XAI\u003c\/li\u003e \u003cli\u003eExaminations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, \u003ci\u003eArtificial Intelligence and Quantum Computing for Advanced Wireless Networks\u003c\/i\u003e is also an indispensable resource for IT support staff, along with policymakers and regulators who work in technology.\u003c\/p\u003e \u003cp\u003ePreface, xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Artificial Intelligence, 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction, 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Motivation, 3\u003c\/p\u003e \u003cp\u003e1.2 Book Structure, 5\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Machine Learning Algorithms, 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Fundamentals, 17\u003c\/p\u003e \u003cp\u003e2.2 ML Algorithm Analysis, 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Artificial Neural Networks, 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Multi-layer Feedforward Neural Networks, 55\u003c\/p\u003e \u003cp\u003e3.2 FIR Architecture, 60\u003c\/p\u003e \u003cp\u003e3.3 Time Series Prediction, 68\u003c\/p\u003e \u003cp\u003e3.4 Recurrent Neural Networks, 69\u003c\/p\u003e \u003cp\u003e3.5 Cellular Neural Networks (CeNN), 81\u003c\/p\u003e \u003cp\u003e3.6 Convolutional Neural Network (CoNN), 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Explainable Neural Networks, 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Explainability Methods, 99\u003c\/p\u003e \u003cp\u003e4.2 Relevance Propagation in ANN, 103\u003c\/p\u003e \u003cp\u003e4.3 Rule Extraction from LSTM Networks, 110\u003c\/p\u003e \u003cp\u003e4.4 Accuracy and Interpretability, 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Graph Neural Networks, 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Concept of Graph Neural Network (GNN), 135\u003c\/p\u003e \u003cp\u003e5.2 Categorization and Modeling of GNN, 144\u003c\/p\u003e \u003cp\u003e5.3 Complexity of NN, 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Learning Equilibria and Games, 179\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Learning in Games, 179\u003c\/p\u003e \u003cp\u003e6.2 Online Learning of Nash Equilibria in Congestion Games, 196\u003c\/p\u003e \u003cp\u003e6.3 Minority Games, 202\u003c\/p\u003e \u003cp\u003e6.4 Nash Q-Learning, 204\u003c\/p\u003e \u003cp\u003e6.5 Routing Games, 211\u003c\/p\u003e \u003cp\u003e6.6 Routing with Edge Priorities, 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 AI Algorithms in Networks, 227\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Review of AI-Based Algorithms in Networks, 227\u003c\/p\u003e \u003cp\u003e7.2 ML for Caching in Small Cell Networks, 237\u003c\/p\u003e \u003cp\u003e7.3 Q-Learning-Based Joint Channel and Power Level Selection in Heterogeneous Cellular Networks, 243\u003c\/p\u003e \u003cp\u003e7.4 ML for Self-Organizing Cellular Networks, 252\u003c\/p\u003e \u003cp\u003e7.5 RL-Based Caching, 267\u003c\/p\u003e \u003cp\u003e7.6 Big Data Analytics in Wireless Networks, 274\u003c\/p\u003e \u003cp\u003e7.7 Graph Neural Networks, 279\u003c\/p\u003e \u003cp\u003e7.8 DRL for Multioperator Network Slicing, 291\u003c\/p\u003e \u003cp\u003e7.9 Deep Q-Learning for Latency-Limited Network Virtualization, 302\u003c\/p\u003e \u003cp\u003e7.10 Multi-Armed Bandit Estimator (MBE), 317\u003c\/p\u003e \u003cp\u003e7.11 Network Representation Learning, 327\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Quantum Computing, 361\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Fundamentals of Quantum Communications, 363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction, 363\u003c\/p\u003e \u003cp\u003e8.2 Quantum Gates and Quantum Computing, 372\u003c\/p\u003e \u003cp\u003e8.3 Quantum Fourier Transform (QFT), 386\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Quantum Channel Information Theory, 397\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Communication Over a Channel, 398\u003c\/p\u003e \u003cp\u003e9.2 Quantum Information Theory, 401\u003c\/p\u003e \u003cp\u003e9.3 Channel Description, 407\u003c\/p\u003e \u003cp\u003e9.4 Channel Classical Capacities, 414\u003c\/p\u003e \u003cp\u003e9.5 Channel Quantum Capacity, 431\u003c\/p\u003e \u003cp\u003e9.6 Quantum Channel Examples, 437\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Quantum Error Correction, 451\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Stabilizer Codes, 458\u003c\/p\u003e \u003cp\u003e10.2 Surface Code, 465\u003c\/p\u003e \u003cp\u003e10.3 Fault-Tolerant Gates, 471\u003c\/p\u003e \u003cp\u003e10.4 Theoretical Framework, 474\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Quantum Search Algorithms, 499\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Quantum Search Algorithms, 499\u003c\/p\u003e \u003cp\u003e11.2 Physics of Quantum Algorithms, 510\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Quantum Machine Learning, 543\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 QML Algorithms, 543\u003c\/p\u003e \u003cp\u003e12.2 QNN Preliminaries, 547\u003c\/p\u003e \u003cp\u003e12.3 Quantum Classifiers with ML: Near-Term Solutions, 550\u003c\/p\u003e \u003cp\u003e12.4 Gradients of Parameterized Quantum Gates, 560\u003c\/p\u003e \u003cp\u003e12.5 Classification with QNNs, 568\u003c\/p\u003e \u003cp\u003e12.6 Quantum Decision Tree Classifier, 575\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 QC Optimization, 593\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Hybrid Quantum-Classical Optimization Algorithms, 593\u003c\/p\u003e \u003cp\u003e13.2 Convex Optimization in Quantum Information Theory, 601\u003c\/p\u003e \u003cp\u003e13.3 Quantum Algorithms for Combinatorial Optimization Problems, 609\u003c\/p\u003e \u003cp\u003e13.4 QC for Linear Systems of Equations, 614\u003c\/p\u003e \u003cp\u003e13.5 Quantum Circuit, 625\u003c\/p\u003e \u003cp\u003e13.6 Quantum Algorithm for Systems of Nonlinear Differential Equations, 628\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Quantum Decision Theory, 637\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Potential Enablers for Qc, 637\u003c\/p\u003e \u003cp\u003e14.2 Quantum Game Theory (QGT), 641\u003c\/p\u003e \u003cp\u003e14.3 Quantum Decision Theory (QDT), 665\u003c\/p\u003e \u003cp\u003e14.4 Predictions in QDT, 676\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Quantum Computing in Wireless Networks, 693\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Quantum Satellite Networks, 693\u003c\/p\u003e \u003cp\u003e15.2 QC Routing for Social Overlay Networks, 706\u003c\/p\u003e \u003cp\u003e15.3 QKD Networks, 713\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Quantum Network on Graph, 733\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Optimal Routing in Quantum Networks, 733\u003c\/p\u003e \u003cp\u003e16.2 Quantum Network on Symmetric Graph, 744\u003c\/p\u003e \u003cp\u003e16.3 QWs, 747\u003c\/p\u003e \u003cp\u003e16.4 Multidimensional QWs, 753\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Quantum Internet, 773\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 System Model, 775\u003c\/p\u003e \u003cp\u003e17.2 Quantum Network Protocol Stack, 789\u003c\/p\u003e \u003cp\u003eReferences, 814\u003c\/p\u003e \u003cp\u003eIndex, 821\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSavo G. Glisic\u003c\/b\u003e is Research Professor at Worcester Polytechnic Institute, Massachusetts, USA. His research interests include network optimization theory, network topology control and graph theory, cognitive networks, game theory, artificial intelligence, and quantum computing technology.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBeatriz Lorenzo\u003c\/b\u003e is Assistant Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst, USA. Her research interests include the areas of communication networks, wireless networks, and mobile computing.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA practical overview of the implementation of artificial intelligence and quantum computing technology in large-scale communication networks\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIncreasingly 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. \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eArtificial Intelligence and Quantum Computing for Advanced Wireless Networks\u003c\/i\u003e, 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.  \u003c\/p\u003e\u003cp\u003eThe 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: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines\u003c\/li\u003e \u003cli\u003eAn 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\u003c\/li\u003e \u003cli\u003eDiscussions of explainable neural networks and XAI \u003c\/li\u003e \u003cli\u003eExaminations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, \u003ci\u003eArtificial Intelligence and Quantum Computing for Advanced Wireless Networks\u003c\/i\u003e is also an indispensable resource for IT support staff, along with policymakers and regulators who work in technology.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988764639461,"sku":"NP9781119790297","price":166.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119790297.jpg?v=1761781502","url":"https:\/\/k12savings.com\/es\/products\/artificial-intelligence-and-quantum-computing-for-advanced-wireless-networks-isbn-9781119790297","provider":"K12savings","version":"1.0","type":"link"}