{"product_id":"quantum-computing-and-machine-learning-for-6g-hardback-9781394238088","title":"Quantum Computing and Machine Learning for 6G (Hardback) 9781394238088","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eQuantum Computing and Machine Learning for 6G\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003ePallavi Sapkale (Edited by), Sapkale (Author), Shilpa Mehta (Edited by), S. Balamurugan (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394238088, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 15 January 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e464 pages\u003cbr\u003e28 x 19 x 2.6 cm, 0.839 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eSecure your expertise in the next frontier of wireless technology with this essential book, which provides a deep dive into the integration of machine learning and quantum computing to build the necessary infrastructure for 6G communication networks.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eDespite the potential benefits of 6G, the technology to enable its realization is not yet available. As a result, the development of technology to solve these challenges must be met before we can start working towards 6G. The primary applications of machine learning within 6G are to create necessary infrastructure advantages as the technology matures. Additionally, 6G communication networks use quantum computing to detect, mitigate, and prevent security vulnerabilities. By integrating machine learning and quantum computing into 5G and 6G technology, intelligent base stations will be able to make decisions for themselves, and mobile devices will be able to create dynamically adaptable clusters based on learned data. This book highlights the role of real-time network learning and the integration of quantum computing, machine learning, and quantum machine learning to enhance service quality. It provides a deep dive into the interplay of these technologies within 6G networks, starting from 5G fundamentals. The book elaborates on how these advanced technologies will underpin 6G’s architecture to meet comprehensive service demands, including those for smart city applications requiring extensive coverage, ultra-low latency, and reliable connectivity. The book details how the synergy between quantum computing, machine learning, and 6G technologies will transform communications, revolutionize markets, and enable groundbreaking applications globally. \u003c\/p\u003e\n\u003cp\u003eReaders will find the volume: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplores real-world scenarios for illustrating the integration of quantum computing and machine learning in 6G;\u003c\/li\u003e\n\u003cli\u003eCovers an extensive range of applications to illustrate the full picture of 6G that implements machine learning and quantum computing approaches;\u003c\/li\u003e\n\u003cli\u003eOffers expert insights through a comprehensive collection of literature reviews and research articles;\u003c\/li\u003e\n\u003cli\u003eIntroduces the interdisciplinary innovations and potential of 6G across multiple industries.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eScientists, industry professionals, researchers, academicians, instructors, and students working in quantum computing and machine learning, especially in the context of advanced wireless communication technology.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAcknowledgement xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Wireless Communication and Transition from 1G to 6G 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKrupali Dhawale, Pranali Bhope, Kunika Dhapodkar and Sejal Kumbhare\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction to Wireless Communication 4\u003c\/p\u003e \u003cp\u003e1.1.1 Definition and Importance of Wireless Communication 4\u003c\/p\u003e \u003cp\u003eImportance of Wireless Communications 4\u003c\/p\u003e \u003cp\u003e1.1.2 Role of Wireless Communication in Connecting People and Devices Globally 5\u003c\/p\u003e \u003cp\u003e1.1.3 Evolution of Wireless Communication Technologies 6\u003c\/p\u003e \u003cp\u003e1.2 Generations of Wireless Communication 8\u003c\/p\u003e \u003cp\u003e1.2.1 1G (Analog Cellular) 8\u003c\/p\u003e \u003cp\u003e1.2.2 2G (Digital Cellular) 9\u003c\/p\u003e \u003cp\u003e1.2.3 3G (Mobile Broadband) 10\u003c\/p\u003e \u003cp\u003e1.2.4 4G (LTE and Beyond) 11\u003c\/p\u003e \u003cp\u003e1.2.5 5G (Next-Generation Connectivity) 12\u003c\/p\u003e \u003cp\u003e1.2.6 Anticipating 6G (Future Evolution) 14\u003c\/p\u003e \u003cp\u003e1.3 1G to 4G: Evolution of Wireless Standards 15\u003c\/p\u003e \u003cp\u003e1.3.1 Overview of 1G to 4G Transitions 15\u003c\/p\u003e \u003cp\u003e1.3.2 Advancements in Digital Modulation and Compression 17\u003c\/p\u003e \u003cp\u003e1.3.3 Shift from Analog to Digital Transmission 19\u003c\/p\u003e \u003cp\u003e1.3.4 Introduction of Data Services and Mobile Internet 20\u003c\/p\u003e \u003cp\u003e1.4 Industry and Research Initiatives for 6G 22\u003c\/p\u003e \u003cp\u003e1.4.1 Involvement of Academia, Industry, and Standardization Bodies 22\u003c\/p\u003e \u003cp\u003e1.4.2 Research Goals and Technological Roadmaps 24\u003c\/p\u003e \u003cp\u003eConclusion 27\u003c\/p\u003e \u003cp\u003eReferences 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The State-of-the-Art and Future Visioning 6G Wireless Network 29\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePayal Bansal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 30\u003c\/p\u003e \u003cp\u003e2.1.1 Heterogeneous Wireless Networks 31\u003c\/p\u003e \u003cp\u003e2.1.2 Vertical Handover 32\u003c\/p\u003e \u003cp\u003e2.2 Handover Management in 6G 34\u003c\/p\u003e \u003cp\u003e2.2.1 History of Handover System 34\u003c\/p\u003e \u003cp\u003e2.2.2 Handover Process 36\u003c\/p\u003e \u003cp\u003e2.2.3 Single-Tier Networks with Handover Skipping Process 38\u003c\/p\u003e \u003cp\u003e2.2.3.1 Coverage Probability 38\u003c\/p\u003e \u003cp\u003e2.2.3.2 Handover Cost 40\u003c\/p\u003e \u003cp\u003e2.2.3.3 Average Throughput 43\u003c\/p\u003e \u003cp\u003e2.3 Two-Tier Network Handover Skipping 43\u003c\/p\u003e \u003cp\u003eBibliography 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Quantum Computing 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Introduction to Quantum Computing 57\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShilpa Mehta and Celestine Iwendi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 57\u003c\/p\u003e \u003cp\u003e3.1.1 Historical Background 58\u003c\/p\u003e \u003cp\u003e3.1.2 Classical Computing vs Quantum Computing 59\u003c\/p\u003e \u003cp\u003e3.1.3 Why Quantum Computers? 60\u003c\/p\u003e \u003cp\u003e3.1.4 Bits versus Qubits 60\u003c\/p\u003e \u003cp\u003e3.1.5 Quantum Registers 61\u003c\/p\u003e \u003cp\u003e3.1.6 Key Principles of Quantum Computing 61\u003c\/p\u003e \u003cp\u003e3.2 Quantum Gates 62\u003c\/p\u003e \u003cp\u003e3.3 Quantum Algorithms 64\u003c\/p\u003e \u003cp\u003e3.3.1 Fourier Transform–Based Algorithms 64\u003c\/p\u003e \u003cp\u003e3.3.1.1 Overview of Discrete Fourier Transform 65\u003c\/p\u003e \u003cp\u003e3.3.1.2 Quantum Fourier Transform 65\u003c\/p\u003e \u003cp\u003e3.3.2 Amplitude Amplification–Based Algorithms 68\u003c\/p\u003e \u003cp\u003e3.3.2.1 Grover’s Algorithm 68\u003c\/p\u003e \u003cp\u003e3.3.2.2 Quantum Counting 69\u003c\/p\u003e \u003cp\u003e3.3.3 Quantum Walk Based Algorithms 69\u003c\/p\u003e \u003cp\u003e3.3.3.1 Boson Sampling Problem 69\u003c\/p\u003e \u003cp\u003e3.3.3.2 Element Distinctness Problem 70\u003c\/p\u003e \u003cp\u003e3.3.3.3 Triangle Finding Problem 70\u003c\/p\u003e \u003cp\u003e3.3.4 Bounded-Error Quantum Polynomial Time Problems 70\u003c\/p\u003e \u003cp\u003e3.3.4.1 Quantum Simulation 71\u003c\/p\u003e \u003cp\u003e3.3.5 Hybrid Algorithms 71\u003c\/p\u003e \u003cp\u003e3.3.5.1 Quantum Approximate Optimization Algorithm 71\u003c\/p\u003e \u003cp\u003e3.3.5.2 Variational Quantum Eigensolver Algorithm 71\u003c\/p\u003e \u003cp\u003e3.3.5.3 Contracted Quantum Eigensolver Algorithm 72\u003c\/p\u003e \u003cp\u003e3.4 Quantum Hardware and Software 72\u003c\/p\u003e \u003cp\u003e3.4.1 Quantum Hardware 72\u003c\/p\u003e \u003cp\u003e3.4.1.1 Types of Quantum Hardware 73\u003c\/p\u003e \u003cp\u003e3.4.2 Quantum Software 74\u003c\/p\u003e \u003cp\u003e3.5 Applications 75\u003c\/p\u003e \u003cp\u003e3.6 Challenges of Quantum Computing 78\u003c\/p\u003e \u003cp\u003e3.7 Current State-of-the-Art 79\u003c\/p\u003e \u003cp\u003e3.8 Summary and Future Scope 85\u003c\/p\u003e \u003cp\u003eReferences 85\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Quantum-Secured Concealed Identifier for 6G Technology 89\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePratham Desai and Dipali Kasat\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 89\u003c\/p\u003e \u003cp\u003e4.1 Quantum Mechanical Properties for Security 90\u003c\/p\u003e \u003cp\u003e4.1.1 Entanglement with Bell-State Example 90\u003c\/p\u003e \u003cp\u003e4.1.2 Entanglement for Bipartite System 92\u003c\/p\u003e \u003cp\u003e4.1.3 No Cloning Theorem 93\u003c\/p\u003e \u003cp\u003e4.2 Quantum Key Distribution Technique (QKD) 96\u003c\/p\u003e \u003cp\u003e4.3 BB84 Algorithm 96\u003c\/p\u003e \u003cp\u003e4.4 Concept of Identifiers 97\u003c\/p\u003e \u003cp\u003e4.5 Drawbacks of Classical Algorithms 99\u003c\/p\u003e \u003cp\u003e4.6 Quantum Concealed Identifiers for 6G Technology 100\u003c\/p\u003e \u003cp\u003e4.6.1 QKD Protocol with a Multiple Coding Basis 100\u003c\/p\u003e \u003cp\u003e4.6.2 Parameters and Basic Equipment 102\u003c\/p\u003e \u003cp\u003e4.6.3 Pseudo-Random Number Seed Key Construction Protocol for Security (PRNSKC) 104\u003c\/p\u003e \u003cp\u003e4.7 A Post-Quantum SUCI for 6G 105\u003c\/p\u003e \u003cp\u003e4.7.1 How SUCI is Vulnerable to Quantum Attacks 105\u003c\/p\u003e \u003cp\u003e4.7.2 Post-Quantum Secure SUCI 106\u003c\/p\u003e \u003cp\u003e4.7.3 Selecting the Perfect KEM for KEMSUCI 107\u003c\/p\u003e \u003cp\u003e4.7.4 Understanding the Kyber Algorithm 109\u003c\/p\u003e \u003cp\u003e4.8 Comparison Between the Existing Schemes 111\u003c\/p\u003e \u003cp\u003eConclusion 112\u003c\/p\u003e \u003cp\u003eBibliography 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Quantum Cryptography: Present and Future 6G 117\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDhananjay Manohar Dakhane, Vaibhav Eknath Narawade and Pallavi Sapkale\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 117\u003c\/p\u003e \u003cp\u003e5.2 Quantum Cryptography 119\u003c\/p\u003e \u003cp\u003e5.3 Quantum Key Distribution 120\u003c\/p\u003e \u003cp\u003e5.4 Post Quantum Cryptography 121\u003c\/p\u003e \u003cp\u003e5.5 Conclusions 121\u003c\/p\u003e \u003cp\u003eReferences 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Network Intelligence with Quantum Computing for 6G 123\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eH. Bhoomeeswaran, G. Joshva Raj, J. Mangaiyarkkarasi and J. Shanthalakshmi Revathy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 124\u003c\/p\u003e \u003cp\u003e6.2 Quantum Computing 127\u003c\/p\u003e \u003cp\u003e6.3 Spintronic QC 127\u003c\/p\u003e \u003cp\u003e6.4 Literature Survey 129\u003c\/p\u003e \u003cp\u003e6.5 Shstno 130\u003c\/p\u003e \u003cp\u003e6.6 Photonic QC 133\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 137\u003c\/p\u003e \u003cp\u003e6.8 Future Scope 138\u003c\/p\u003e \u003cp\u003eReferences 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Machine Learning 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Introduction to Machine Learning: Conceptualization, Implementation, and Research Perspective 143\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSnehasis Dey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction to Machine Learning: Conceptualization Perspective 144\u003c\/p\u003e \u003cp\u003e7.1.1 Basics of Machine Learning 144\u003c\/p\u003e \u003cp\u003e7.1.2 Literature Survey 147\u003c\/p\u003e \u003cp\u003e7.1.3 Problem Statement and Proposed Model 147\u003c\/p\u003e \u003cp\u003e7.1.4 Evolution of Machine Learning 148\u003c\/p\u003e \u003cp\u003e7.1.5 Machine Learning as a Powerful Tool for Future Advancement 150\u003c\/p\u003e \u003cp\u003e7.2 A Dive Into Machine Learning: Implementation Perspective 151\u003c\/p\u003e \u003cp\u003e7.2.1 Correlations and Differences Between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) 151\u003c\/p\u003e \u003cp\u003e7.2.2 Learning Techniques in Machine Learning 153\u003c\/p\u003e \u003cp\u003e7.2.3 Algorithms in Machine Learning 155\u003c\/p\u003e \u003cp\u003e7.3 Recent Trends in Machine Learning: Research Perspective 156\u003c\/p\u003e \u003cp\u003e7.3.1 Machine Learning in Fourth Industry Revolution or Industry 4.0 (4IR) 157\u003c\/p\u003e \u003cp\u003e7.3.2 Machine Learning in Real-World Applications: ml\u003c\/p\u003e \u003cp\u003efor Everything, for Everywhere and for Everyone 157\u003c\/p\u003e \u003cp\u003e7.3.3 Machine Learning in 5G Wireless Communications and Beyond 158\u003c\/p\u003e \u003cp\u003e7.4 Conclusion 159\u003c\/p\u003e \u003cp\u003eReferences 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 6G Wireless Networks: Pioneering with Machine Learning Technologies 161\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKrupali Dhawale, Shraddha Jha, Mishri Gube, Shivraj Guduri and Khwaish Asati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 162\u003c\/p\u003e \u003cp\u003e8.2 Introduction to 6G Wireless Networks and Machine Learning 162\u003c\/p\u003e \u003cp\u003e8.2.1 6G Wireless Network and Its Significance 162\u003c\/p\u003e \u003cp\u003e8.2.2 Challenges for 6G Networks 164\u003c\/p\u003e \u003cp\u003e8.2.3 Goals for 6G Networks 166\u003c\/p\u003e \u003cp\u003e8.2.4 Introduction to Machine Learning and Its Relevance in Wireless Networks 167\u003c\/p\u003e \u003cp\u003e8.2.4.1 Importance of Machine Learning in Wireless Networks 168\u003c\/p\u003e \u003cp\u003e8.2.5 Potential Benefits of Integrating Machine Learning in 6G Technology 169\u003c\/p\u003e \u003cp\u003e8.3 Machine Learning Techniques for 6G Wireless Networks 171\u003c\/p\u003e \u003cp\u003e8.3.1 Signal Processing and Optimization 171\u003c\/p\u003e \u003cp\u003e8.3.2 Adaptive Beamforming and Spatial Processing Using Machine Learning 173\u003c\/p\u003e \u003cp\u003e8.3.3 Benefits of Adaptive Rendering and Spatial Processing through Machine Learning 174\u003c\/p\u003e \u003cp\u003e8.3.4 Signal Denoising, Interference Mitigation, and Resource Allocation 174\u003c\/p\u003e \u003cp\u003e8.3.5 Spectrum Management and Allocation 176\u003c\/p\u003e \u003cp\u003e8.4 Driven Network Management and Security 178\u003c\/p\u003e \u003cp\u003e8.4.1 Self-Organizing Networks (SON) 178\u003c\/p\u003e \u003cp\u003e8.4.2 Automatic Network Configuration and Optimization through AI 180\u003c\/p\u003e \u003cp\u003e8.4.2.1 Network Management 180\u003c\/p\u003e \u003cp\u003e8.4.2.2 Network Security 181\u003c\/p\u003e \u003cp\u003e8.4.3 Fault Detection, Self-Healing, and Network Maintenance 181\u003c\/p\u003e \u003cp\u003e8.5 Challenges and Future Directions 182\u003c\/p\u003e \u003cp\u003e8.5.1 Data Privacy and Ethics Issues and Challenges 182\u003c\/p\u003e \u003cp\u003e8.5.2 Future Directions in Data Privacy and Ethical Considerations 183\u003c\/p\u003e \u003cp\u003e8.5.3 Balancing Data Usage and User Privacy in AI-Driven Networks 184\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 185\u003c\/p\u003e \u003cp\u003eReferences 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Machine Learning–Based Communication and Network Automation: Advancements, Challenges, and Prospects 187\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJ. Shanthalakshmi Revathy and J. Mangaiyarkkarasi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 188\u003c\/p\u003e \u003cp\u003e9.2 Advancements in Machine Learning for Communication and Network Automation 189\u003c\/p\u003e \u003cp\u003e9.2.1 Machine Learning Fundamentals 190\u003c\/p\u003e \u003cp\u003e9.2.1.1 Supervised, Unsupervised, and Reinforcement Learning in Network Automation 190\u003c\/p\u003e \u003cp\u003e9.2.2 Applications in Network Automation 192\u003c\/p\u003e \u003cp\u003e9.2.2.1 Predictive Maintenance and Fault Detection 192\u003c\/p\u003e \u003cp\u003e9.2.2.2 Quality of Service (QoS) Optimization 193\u003c\/p\u003e \u003cp\u003e9.2.2.3 Traffic Engineering and Load Balancing 193\u003c\/p\u003e \u003cp\u003e9.2.3 Data Sources and Preprocessing 194\u003c\/p\u003e \u003cp\u003e9.2.3.1 Data Collection Methods in Network Environments 194\u003c\/p\u003e \u003cp\u003e9.2.3.2 Data Preprocessing Techniques 195\u003c\/p\u003e \u003cp\u003e9.2.3.3 Feature Selection and Engineering 195\u003c\/p\u003e \u003cp\u003e9.2.4 Model Training and Deployment 196\u003c\/p\u003e \u003cp\u003e9.3 Challenges in Implementing Machine Learning for Network Automation 199\u003c\/p\u003e \u003cp\u003e9.3.1 Data Quality and Availability 199\u003c\/p\u003e \u003cp\u003e9.3.2 Scalability and Resource Constraints 200\u003c\/p\u003e \u003cp\u003e9.3.3 Interoperability and Standards 201\u003c\/p\u003e \u003cp\u003e9.3.3.1 Need for Standardization 201\u003c\/p\u003e \u003cp\u003e9.3.3.2 Compatibility with Existing Network Infrastructure 202\u003c\/p\u003e \u003cp\u003e9.3.3.3 Vendor-Specific Challenges 202\u003c\/p\u003e \u003cp\u003e9.3.4 Ethical and Regulatory Considerations 203\u003c\/p\u003e \u003cp\u003e9.3.4.1 Bias and Fairness in Machine Learning Algorithms 203\u003c\/p\u003e \u003cp\u003e9.3.4.2 Regulatory Compliance in Network Automation 204\u003c\/p\u003e \u003cp\u003e9.3.4.3 Ethical Implications of Automation in Communication 205\u003c\/p\u003e \u003cp\u003e9.4 Prospects and Future Directions 206\u003c\/p\u003e \u003cp\u003e9.4.1 Emerging Technologies 206\u003c\/p\u003e \u003cp\u003e9.4.2 AI-Driven Autonomous Networks 208\u003c\/p\u003e \u003cp\u003e9.4.2.1 Toward Fully Autonomous Networks 208\u003c\/p\u003e \u003cp\u003e9.4.2.2 Self-Healing and Self-Optimizing Networks 208\u003c\/p\u003e \u003cp\u003e9.4.2.3 Human-Machine Collaboration in Network Management 208\u003c\/p\u003e \u003cp\u003e9.5 Research and Development Trends 209\u003c\/p\u003e \u003cp\u003e9.5.1 Current Research Trends in Machine Learning and Network Automation 209\u003c\/p\u003e \u003cp\u003e9.5.2 Industry Collaborations and Academic Contributions 210\u003c\/p\u003e \u003cp\u003e9.5.3 The Importance of Open-Source Projects 211\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 212\u003c\/p\u003e \u003cp\u003eReferences 213\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Empowering 6G Communication Systems: Harnessing Machine Learning for Advancements in Flexible and 3D-Printed Antennas 217\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDuygu Nazan Gençoğlan and Shilpa Mehta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 218\u003c\/p\u003e \u003cp\u003e10.2 Flexible and 3D-Printed Antennas 222\u003c\/p\u003e \u003cp\u003e10.3 Challenges in 6G Antenna Design 224\u003c\/p\u003e \u003cp\u003e10.4 Machine Learning for Antenna Design 225\u003c\/p\u003e \u003cp\u003e10.5 Data-Driven Antenna Optimization 226\u003c\/p\u003e \u003cp\u003e10.6 Topology Optimization with ml 227\u003c\/p\u003e \u003cp\u003e10.7 Material Selection and Optimization 229\u003c\/p\u003e \u003cp\u003e10.8 Simulation and Modeling with ml 230\u003c\/p\u003e \u003cp\u003e10.9 Hardware-Software Co-Design for ML-Aided Antennas 231\u003c\/p\u003e \u003cp\u003e10.10 Experimental Validation and Prototyping 232\u003c\/p\u003e \u003cp\u003e10.11 Conclusion and Future Directions 232\u003c\/p\u003e \u003cp\u003e10.12 Future Directions 233\u003c\/p\u003e \u003cp\u003eReferences 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Potential Communication in B5G Networks Through Hybrid Millimeter-Wave Beamforming and Machine Learning: Basics, Challenges, and Future Path 243\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSnehasis Dey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 244\u003c\/p\u003e \u003cp\u003e11.2 Literature Survey 245\u003c\/p\u003e \u003cp\u003e11.3 HBF Open Challenges 251\u003c\/p\u003e \u003cp\u003e11.4 Conclusion 258\u003c\/p\u003e \u003cp\u003eBibliography 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Device-to-Device Communication in 6G Using Machine Learning 261\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJ. Shanthalakshmi Revathy, J. Mangaiyarkkarasi and J. Matcha Rani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 262\u003c\/p\u003e \u003cp\u003e12.2 Fundamentals of Device-to-Device Communication 263\u003c\/p\u003e \u003cp\u003e12.3 Evolution from Previous Generations 265\u003c\/p\u003e \u003cp\u003e12.3.1 Early Foundations: Peer-to-Peer and Ad Hoc Networks 265\u003c\/p\u003e \u003cp\u003e12.3.2 Device-to-Device Communications in Cellular Networks 266\u003c\/p\u003e \u003cp\u003e12.3.3 The 5G Era 267\u003c\/p\u003e \u003cp\u003e12.3.4 Enhancement in 6G 267\u003c\/p\u003e \u003cp\u003e12.4 Role of Machine Learning in 6G D2D Communication 268\u003c\/p\u003e \u003cp\u003e12.4.1 Supervised Learning 268\u003c\/p\u003e \u003cp\u003e12.4.2 Unsupervised Learning 269\u003c\/p\u003e \u003cp\u003e12.4.3 Reinforcement Learning 270\u003c\/p\u003e \u003cp\u003e12.4.4 Integration of Machine Learning in 6G Networks 271\u003c\/p\u003e \u003cp\u003e12.5 Applications of Machine Learning in D2D Communication Resource Allocation and Spectrum Management 273\u003c\/p\u003e \u003cp\u003e12.6 Challenges and Solutions 275\u003c\/p\u003e \u003cp\u003e12.7 Case Studies 277\u003c\/p\u003e \u003cp\u003e12.7.1 Smart Cities and Urban IoT Networks 277\u003c\/p\u003e \u003cp\u003e12.7.2 Autonomous Vehicles and Vehicular Communication 278\u003c\/p\u003e \u003cp\u003e12.7.3 Healthcare and Wearable Devices 278\u003c\/p\u003e \u003cp\u003e12.7.4 Augmented Reality (AR) and Immersive Media 279\u003c\/p\u003e \u003cp\u003e12.8 Challenges and Future Scope 279\u003c\/p\u003e \u003cp\u003e12.9 Conclusion 280\u003c\/p\u003e \u003cp\u003eReferences 281\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: Quantum Computing and Machine Learning 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Integrating Quantum Computing and Machine Learning in 6G Networks 285\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eOgobuchi D. Okey, Theodore T. Chiagunye, Henrietta U. Udeani, Ikechukwu Nicholas, Renata L. Rosa and Demóstenes R. Zegarra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 286\u003c\/p\u003e \u003cp\u003e13.2 Background Study 288\u003c\/p\u003e \u003cp\u003e13.2.1 Technology Evolutionary Trends Toward 6G Network 288\u003c\/p\u003e \u003cp\u003e13.2.2 Unique Features of 6G Networks 289\u003c\/p\u003e \u003cp\u003e13.2.3 The Principle of Quantum Computing 291\u003c\/p\u003e \u003cp\u003e13.2.4 Machine Learning 292\u003c\/p\u003e \u003cp\u003e13.3 Quantum Machine Learning Algorithms and Implementation Frameworks 294\u003c\/p\u003e \u003cp\u003e13.4 Resource Allocation in QML-Enabled 6G Network 300\u003c\/p\u003e \u003cp\u003e13.5 Security Challenges and Prospects in QML 6G 301\u003c\/p\u003e \u003cp\u003e13.6 Limitations, Benefits, and Future Directions 303\u003c\/p\u003e \u003cp\u003e13.7 Conclusion 305\u003c\/p\u003e \u003cp\u003eReferences 305\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 A Quantum Computing Perspective in 6G Networks: The Challenge of Adaptive Network Intelligence 311\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePallavi Sapkale\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 312\u003c\/p\u003e \u003cp\u003e14.1.1 Quantum Computing in 6G 312\u003c\/p\u003e \u003cp\u003e14.2 What is Network Intelligence in Quantum Computing? 313\u003c\/p\u003e \u003cp\u003e14.2.1 Methods to Achieve the Network Intelligence in Quantum Computing 317\u003c\/p\u003e \u003cp\u003e14.3 How to Accomplish Network Intelligence 319\u003c\/p\u003e \u003cp\u003e14.4 Quantum Computing Opportunities with 6G 319\u003c\/p\u003e \u003cp\u003e14.5 Challenges and Research Scope in Quantum Computing with 6G 320\u003c\/p\u003e \u003cp\u003e14.5.1 Main Challenges in Quantum Computing with 6G 320\u003c\/p\u003e \u003cp\u003e14.5.2 Research Scope in Quantum Computing 322\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 323\u003c\/p\u003e \u003cp\u003eReferences 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Role of QML in 6G Integrated Vehicular Networks 327\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eR. Palanivel, Muthulakshmi P., Snehasis Dey, Shilpa Mehta and Pallavi Sapkale\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 328\u003c\/p\u003e \u003cp\u003e15.2 Literature Survey 331\u003c\/p\u003e \u003cp\u003e15.3 Methodology 332\u003c\/p\u003e \u003cp\u003e15.3.1 Quantum Machine Learning for Traffic Prediction in 6G Networks 332\u003c\/p\u003e \u003cp\u003e15.3.1.1 Environment Setup 333\u003c\/p\u003e \u003cp\u003e15.3.1.2 Quantum Traffic Prediction Model 333\u003c\/p\u003e \u003cp\u003e15.3.1.3 Quantum Circuit Representation 333\u003c\/p\u003e \u003cp\u003e15.3.1.4 Safety Analysis 334\u003c\/p\u003e \u003cp\u003e15.3.1.5 Interpretation 334\u003c\/p\u003e \u003cp\u003e15.3.2 QML for Network Security in Vehicular Communication 336\u003c\/p\u003e \u003cp\u003e15.3.2.1 Environment Setup 336\u003c\/p\u003e \u003cp\u003e15.3.2.2 Quantum Key Distribution (QKD) 337\u003c\/p\u003e \u003cp\u003e15.3.2.3 Node1’s Side (Initialization) 337\u003c\/p\u003e \u003cp\u003e15.3.2.4 Quantum Communication Channel (Simulated) 337\u003c\/p\u003e \u003cp\u003e15.3.2.5 Node2’s Measurement 338\u003c\/p\u003e \u003cp\u003e15.3.2.6 Security and Key Sharing 338\u003c\/p\u003e \u003cp\u003e15.3.2.7 Interpretation 338\u003c\/p\u003e \u003cp\u003e15.3.3 6G Network Slicing and Resource Management by QML 339\u003c\/p\u003e \u003cp\u003e15.3.3.1 Environment Setup 339\u003c\/p\u003e \u003cp\u003e15.3.3.2 Implementation Process 340\u003c\/p\u003e \u003cp\u003e15.3.3.3 Interpretation 341\u003c\/p\u003e \u003cp\u003e15.3.4 Quality-of-Service (QoS) Optimization by QML 342\u003c\/p\u003e \u003cp\u003e15.3.4.1 Environment Setup and Parameters 342\u003c\/p\u003e \u003cp\u003e15.3.4.2 Implementation Process 342\u003c\/p\u003e \u003cp\u003e15.3.4.3 Interpretation 343\u003c\/p\u003e \u003cp\u003e15.4 Results and Discussion 344\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 346\u003c\/p\u003e \u003cp\u003eReferences 346\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V: Applications 349\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Smart Irrigation Technique Using IoT Based on 5G 351\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJyoti B. Deone and Khan Rahat Afreen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 352\u003c\/p\u003e \u003cp\u003e16.2 Related Work 353\u003c\/p\u003e \u003cp\u003e16.3 5G Network on Smart Farming 357\u003c\/p\u003e \u003cp\u003e16.3.1 The Following Decade Will See the Development of 5G and Smart Farming 358\u003c\/p\u003e \u003cp\u003e16.4 Proposed Methodology 359\u003c\/p\u003e \u003cp\u003e16.5 Working Modules of the System 360\u003c\/p\u003e \u003cp\u003e16.5.1 Login and Registration Module 360\u003c\/p\u003e \u003cp\u003e16.5.2 Change Number Module 360\u003c\/p\u003e \u003cp\u003e16.5.3 Check Status Module 360\u003c\/p\u003e \u003cp\u003e16.5.4 Start Water Pump Module 360\u003c\/p\u003e \u003cp\u003e16.5.5 Stop Water Pump Module 361\u003c\/p\u003e \u003cp\u003e16.5.6 Force Start Water Pump Module 361\u003c\/p\u003e \u003cp\u003e16.5.7 Auto Stopped Module 361\u003c\/p\u003e \u003cp\u003e16.6 Experimental Result Analysis and Working 361\u003c\/p\u003e \u003cp\u003e16.7 Conclusion 364\u003c\/p\u003e \u003cp\u003eReferences 364\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Modeling and Development of Low-Cost Visible Light Communication System 367\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMrinmoyee Mukherjee, Kevin Noronha and Ravi Kumar Bandi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Learning Objectives 368\u003c\/p\u003e \u003cp\u003e17.2 Introduction to VLC 368\u003c\/p\u003e \u003cp\u003e17.3 VLC System Description 374\u003c\/p\u003e \u003cp\u003e17.3.1 Key Parameters - Light-Emitting Diode 375\u003c\/p\u003e \u003cp\u003e17.3.2 Key Parameters - Photodiode 378\u003c\/p\u003e \u003cp\u003e17.3.3 Key Parameters - VLC Channel 379\u003c\/p\u003e \u003cp\u003e17.4 Experimental Implementation of the VLC System 382\u003c\/p\u003e \u003cp\u003e17.4.1 Block Diagram and Technical Specifications 382\u003c\/p\u003e \u003cp\u003e17.4.2 Results and Discussions 390\u003c\/p\u003e \u003cp\u003e17.5 Simulation and Modeling of the VLC System 392\u003c\/p\u003e \u003cp\u003eReferences 418\u003c\/p\u003e \u003cp\u003eIndex 423\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433232068888,"sku":"9781394238088","price":160.85,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394238088.jpg?v=1784852341","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/quantum-computing-and-machine-learning-for-6g-hardback-9781394238088","provider":"Freshly Printed Books","version":"1.0","type":"link"}