{"product_id":"current-and-future-cellular-systems-isbn-9781394256044","title":"Current and Future Cellular Systems","description":"\u003cp\u003e\u003cb\u003eComprehensive reference on the latest trends, solutions, challenges, and future directions of 5G communications and beyond\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eCurrent and Future Cellular Systems: Technologies, Applications, and Challenges\u003c\/i\u003e covers the state of the art in architectures and solutions for 5G wireless communication and beyond. This book is unique because instead of focusing on singular topics, it considers various technologies being used in conjunction with 5G and beyond 5G technologies. All new and emerging technologies are covered, along with their problems and how quality of service (QoS) can be improved with respect to future requirements. \u003c\/p\u003e\u003cp\u003eThis book highlights the latest trends in resource allocation techniques due to different device (or user) characteristics, provides a special focus on wide bandwidth millimeter wave communications including circuitry, antennas, and propagation, and discusses the involvement of decision-making processes assisted by artificial intelligence\/machine learning (AI\/ML) in applications such as resource allocation, power allocation, QoS improvement, and autonomous vehicles. Readers will also learn to develop mathematical modeling, perform simulation setup, and configure parameters related to simulations. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eCurrent and Future Cellular Systems\u003c\/i\u003e includes information on: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eThe Internet of Vehicles (IoV), covering requirements, challenges, and limitations of Cellular Vehicle-to-Everything (C-V2X) with Resource Allocation (RA) techniques\u003c\/li\u003e\n\u003cli\u003eIntelligent reflecting surfaces, unmanned aerial vehicles, power optimized frameworks, challenges in a sub-6 GHz band, and communication in a THz band\u003c\/li\u003e\n\u003cli\u003eThe role of IoT in healthcare, agriculture, smart home applications, networking requirements, and the metaverse\u003c\/li\u003e\n\u003cli\u003eQuantum computing, cloud computing, spectrum sharing methods, and performance analysis of WiFi 6\/7 for indoor and outdoor environments\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eProviding expansive yet accessible coverage of the subject by exploring both basic and advanced topics, \u003ci\u003eCurrent and Future Cellular Systems\u003c\/i\u003e serves as an excellent introduction to the fundamentals of 5G and its applications for graduate students, researchers, and industry professionals in the field of wireless communication technologies. \u003c\/p\u003e\u003cp\u003eAbout the Editors xvii\u003c\/p\u003e \u003cp\u003eList of Contributors xix\u003c\/p\u003e \u003cp\u003ePreface xxv\u003c\/p\u003e \u003cp\u003eGlossary xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction xxix\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Spectrum Sharing Schemes for 5G and Beyond in Wireless Communication 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAditya Bakshi, Akhil Gupta, and Arushi Pandey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.1.1 Motivation 2\u003c\/p\u003e \u003cp\u003e1.1.2 Literature Review 2\u003c\/p\u003e \u003cp\u003e1.2 Spectrum Sharing Technologies 6\u003c\/p\u003e \u003cp\u003e1.2.1 Machine Learning in Spectrum Sharing 7\u003c\/p\u003e \u003cp\u003e1.2.2 Cooperative and Cognitive Radio Networks 9\u003c\/p\u003e \u003cp\u003e1.2.2.1 Integration of Cooperative and Cognitive Radio Networks 10\u003c\/p\u003e \u003cp\u003e1.2.3 Interference Mitigation Strategies 10\u003c\/p\u003e \u003cp\u003e1.3 Case Study and Performance Evaluation 12\u003c\/p\u003e \u003cp\u003e1.4 Future Trends and Challenges 14\u003c\/p\u003e \u003cp\u003e1.4.1 Challenges Facing Wireless Communication 15\u003c\/p\u003e \u003cp\u003e1.5 Conclusion 16\u003c\/p\u003e \u003cp\u003eReferences 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Synergizing 5G, IoT, and Deep Learning: Pioneering Technological Integration for a Connected Future 21\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnkita Sharma and Shalli Rani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 21\u003c\/p\u003e \u003cp\u003e2.2 Security Threats on 5G Network 22\u003c\/p\u003e \u003cp\u003e2.3 Applications of 5G 24\u003c\/p\u003e \u003cp\u003e2.4 Advanced Intrusion Detection Systems (IDS) 25\u003c\/p\u003e \u003cp\u003e2.5 Integration of 5G-IoT-DL 25\u003c\/p\u003e \u003cp\u003e2.6 Security Challenges 26\u003c\/p\u003e \u003cp\u003e2.7 Role of ML and DL in 5G at Application and Infra Level 27\u003c\/p\u003e \u003cp\u003e2.8 Conclusion 29\u003c\/p\u003e \u003cp\u003eReferences 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Driving Next Generation IoT with 5G and Beyond 33\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShishir Shrivastava, Ankita Rana, and Ashu Taneja\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 33\u003c\/p\u003e \u003cp\u003e3.2 Need for Technological Advancement 35\u003c\/p\u003e \u003cp\u003e3.3 Existing Wireless Technologies 35\u003c\/p\u003e \u003cp\u003e3.4 Challenges in Existing Technologies 37\u003c\/p\u003e \u003cp\u003e3.5 Towards 5G Communication 39\u003c\/p\u003e \u003cp\u003e3.5.1 MIMO and Massive MIMO 39\u003c\/p\u003e \u003cp\u003e3.5.2 Millimeter Wave (mmWave) Communication 42\u003c\/p\u003e \u003cp\u003e3.5.3 Small Cells 43\u003c\/p\u003e \u003cp\u003e3.5.4 Visible Light Communication 44\u003c\/p\u003e \u003cp\u003e3.6 IoT and its Evolution 45\u003c\/p\u003e \u003cp\u003e3.7 Role of 5G in IoT 46\u003c\/p\u003e \u003cp\u003e3.8 Integration of 5G IoT with Other Technologies 47\u003c\/p\u003e \u003cp\u003e3.8.1 Ai\/ml 50\u003c\/p\u003e \u003cp\u003e3.8.2 Cloud Computing 50\u003c\/p\u003e \u003cp\u003e3.8.3 Fog Computing 51\u003c\/p\u003e \u003cp\u003e3.8.4 Digital Twin 52\u003c\/p\u003e \u003cp\u003e3.8.4.1 Digital Twin Lifecycle: From Data to Transformation 53\u003c\/p\u003e \u003cp\u003e3.9 Techniques to Improve the Performance of Wireless Networks 55\u003c\/p\u003e \u003cp\u003e3.10 Performance Parameters of Next Generation Wireless Systems 58\u003c\/p\u003e \u003cp\u003e3.10.1 The Elaborate Rhythm of Performance Indicators 60\u003c\/p\u003e \u003cp\u003e3.11 Challenges and Future Directions 60\u003c\/p\u003e \u003cp\u003e3.12 Conclusion 61\u003c\/p\u003e \u003cp\u003eReferences 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Emerging Communication Paradigms for 6G IoT: Challenges and Opportunities 65\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAditya Soni, Ashu Taneja, Neeti Taneja, and Laith Abualigah\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 65\u003c\/p\u003e \u003cp\u003e4.1.1 Breakthrough 6G Technologies 68\u003c\/p\u003e \u003cp\u003e4.1.1.1 Holographic MIMO (Multiple Input Multiple Output) 68\u003c\/p\u003e \u003cp\u003e4.1.1.2 Intelligent Reflecting Surfaces (IRSs) 68\u003c\/p\u003e \u003cp\u003e4.1.1.3 Cell free Massive MIMO 69\u003c\/p\u003e \u003cp\u003e4.1.1.4 Edge Computing 70\u003c\/p\u003e \u003cp\u003e4.1.1.5 Terahertz (THz) Communication 70\u003c\/p\u003e \u003cp\u003e4.1.1.6 Quantum Communication 71\u003c\/p\u003e \u003cp\u003e4.2 Internet-of-Things and its Evolution 71\u003c\/p\u003e \u003cp\u003e4.2.1 Role of 6G IoT 71\u003c\/p\u003e \u003cp\u003e4.2.2 6G IoT Framework 72\u003c\/p\u003e \u003cp\u003e4.3 Enabling 6G Technologies for IoT 73\u003c\/p\u003e \u003cp\u003e4.3.1 Convergence with Other Key Technologies 75\u003c\/p\u003e \u003cp\u003e4.3.1.1 Advancing Beyond Sub-6 GHz Towards THz Communication 76\u003c\/p\u003e \u003cp\u003e4.3.1.2 Artificial Intelligence and Advanced Machine Learning 76\u003c\/p\u003e \u003cp\u003e4.3.1.3 Compressive Sensing 76\u003c\/p\u003e \u003cp\u003e4.3.1.4 Blockchain\/Distributed Ledger Technology 77\u003c\/p\u003e \u003cp\u003e4.3.1.5 Digital Twin 77\u003c\/p\u003e \u003cp\u003e4.3.1.6 Intelligent Edge Computing 77\u003c\/p\u003e \u003cp\u003e4.3.1.7 Dynamic Network Slicing 78\u003c\/p\u003e \u003cp\u003e4.3.1.8 Big Data Analytics 78\u003c\/p\u003e \u003cp\u003e4.3.1.9 Wireless Information and Power Transfer (WIPT) 78\u003c\/p\u003e \u003cp\u003e4.3.1.10 Backscatter Communication 79\u003c\/p\u003e \u003cp\u003e4.3.1.11 Communication-Computing-Control Convergence 79\u003c\/p\u003e \u003cp\u003e4.4 Use Case Scenarios 80\u003c\/p\u003e \u003cp\u003e4.4.1 Smart Healthcare 80\u003c\/p\u003e \u003cp\u003e4.4.2 Smart Transportation 81\u003c\/p\u003e \u003cp\u003e4.4.3 Smart Manufacturing 82\u003c\/p\u003e \u003cp\u003e4.4.4 Smart Agriculture 83\u003c\/p\u003e \u003cp\u003e4.4.5 Smart Classrooms 83\u003c\/p\u003e \u003cp\u003e4.4.6 Smart Cities 84\u003c\/p\u003e \u003cp\u003e4.5 Challenges Faced and the Solutions Offered 85\u003c\/p\u003e \u003cp\u003e4.6 Conclusion 86\u003c\/p\u003e \u003cp\u003eReferences 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Securing the Internet of Things: Cybersecurity Challenges, Strategies, and Future Directions in the Era of 5G and Edge Computing 89\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGeetanshi, Harshit Manocha, Himanshi Babbar, and Cherry Mangla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 89\u003c\/p\u003e \u003cp\u003e5.1.1 History of IoT and Edge Computing in 5G 94\u003c\/p\u003e \u003cp\u003e5.2 Literature Review 95\u003c\/p\u003e \u003cp\u003e5.3 Applications in IoT and Edge Computing 95\u003c\/p\u003e \u003cp\u003e5.4 Cybersecurity Management System for IoT Environments 97\u003c\/p\u003e \u003cp\u003e5.4.1 Security Layers 97\u003c\/p\u003e \u003cp\u003e5.5 Current Cyber Security Strategies in IoT 99\u003c\/p\u003e \u003cp\u003e5.6 IoT Cybersecurity’s Role in Reshaping Machine Learning 100\u003c\/p\u003e \u003cp\u003e5.6.1 Role of IoT in Artificial Intelligence 101\u003c\/p\u003e \u003cp\u003e5.7 Real Life Scenario 102\u003c\/p\u003e \u003cp\u003e5.8 Conclusions 105\u003c\/p\u003e \u003cp\u003eReferences 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Autonomous Systems for 5G Networks: A Comprehensive Analysis of Features Toward Generalization and Adaptability 107\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDurga Shankar Baggam and Shalli Rani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 107\u003c\/p\u003e \u003cp\u003e6.2 Survey Method 109\u003c\/p\u003e \u003cp\u003e6.3 Background and Related Works 113\u003c\/p\u003e \u003cp\u003e6.3.1 Autonomous System Architecture 114\u003c\/p\u003e \u003cp\u003e6.3.1.1 Application Layer 120\u003c\/p\u003e \u003cp\u003e6.3.1.2 Cognitive Layer 120\u003c\/p\u003e \u003cp\u003e6.3.1.3 Perception Layer 120\u003c\/p\u003e \u003cp\u003e6.3.1.4 Physical Layer 120\u003c\/p\u003e \u003cp\u003e6.3.2 Sensors 121\u003c\/p\u003e \u003cp\u003e6.3.3 Artificial Intelligence Techniques 121\u003c\/p\u003e \u003cp\u003e6.3.4 Intelligent Transport System (ITS) 124\u003c\/p\u003e \u003cp\u003e6.3.5 B5G-Based Vehicular Telecommunication 125\u003c\/p\u003e \u003cp\u003e6.4 Discussion 126\u003c\/p\u003e \u003cp\u003e6.4.1 Environmental Uncertainties 128\u003c\/p\u003e \u003cp\u003e6.4.2 Security Challenges and Counter Measures 129\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 129\u003c\/p\u003e \u003cp\u003eReferences 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Integrated Trends, Opportunities, and Challenges of 5G and Internet of Things 139\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eEkta Dixit and Shalli Rani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 139\u003c\/p\u003e \u003cp\u003e7.1.1 Overview of 5G 140\u003c\/p\u003e \u003cp\u003e7.1.2 Evolution from 1G to 5G 141\u003c\/p\u003e \u003cp\u003e7.1.3 5G Architecture 141\u003c\/p\u003e \u003cp\u003e7.1.4 Overview of IoT 143\u003c\/p\u003e \u003cp\u003e7.1.5 Features of IoT 143\u003c\/p\u003e \u003cp\u003e7.1.5.1 Avalability 143\u003c\/p\u003e \u003cp\u003e7.1.5.2 Mobility 143\u003c\/p\u003e \u003cp\u003e7.1.5.3 Scalabilty 143\u003c\/p\u003e \u003cp\u003e7.1.5.4 Security 144\u003c\/p\u003e \u003cp\u003e7.1.5.5 Context Awareness 144\u003c\/p\u003e \u003cp\u003e7.1.6 IoT Architecture 144\u003c\/p\u003e \u003cp\u003e7.1.6.1 Application Layer 144\u003c\/p\u003e \u003cp\u003e7.1.6.2 Network Layer 144\u003c\/p\u003e \u003cp\u003e7.1.6.3 Edge Layer 145\u003c\/p\u003e \u003cp\u003e7.2 Requirements for Integration of 5G with IoT 145\u003c\/p\u003e \u003cp\u003e7.2.1 Integrated 5G IoT Layered Architecture 145\u003c\/p\u003e \u003cp\u003e7.3 Opportunities of 5G integrated IoT 146\u003c\/p\u003e \u003cp\u003e7.3.1 Smart Cities 146\u003c\/p\u003e \u003cp\u003e7.3.2 Smart Vehicles 146\u003c\/p\u003e \u003cp\u003e7.3.3 Device to Device Communications 147\u003c\/p\u003e \u003cp\u003e7.3.4 Business 147\u003c\/p\u003e \u003cp\u003e7.3.5 Satelite and Aerial Research 147\u003c\/p\u003e \u003cp\u003e7.3.6 Video Surveillance 147\u003c\/p\u003e \u003cp\u003e7.4 Challenges of 5G Integrated IoT 147\u003c\/p\u003e \u003cp\u003e7.4.1 Insufficient Control over Data Storage and Usage 148\u003c\/p\u003e \u003cp\u003e7.4.2 Scalability 148\u003c\/p\u003e \u003cp\u003e7.4.3 Heterogeneity of 5G and IoT Data 148\u003c\/p\u003e \u003cp\u003e7.4.4 Blockchain Processing Time 148\u003c\/p\u003e \u003cp\u003e7.4.5 5G mm-Wave Issues 149\u003c\/p\u003e \u003cp\u003e7.4.6 Threat Protection of 5G IoT 149\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 149\u003c\/p\u003e \u003cp\u003eReferences 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Advancement in Resource Allocation for Future Generation of Communications 153\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGarima Chopra and Suhaib Ahmed\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 153\u003c\/p\u003e \u003cp\u003e8.2 Current Trends in Multiple Access Techniques 154\u003c\/p\u003e \u003cp\u003e8.3 Scheduling Algorithms for 5G\/Beyond 5G 155\u003c\/p\u003e \u003cp\u003e8.4 Factors Influencing Scheduling Algorithms 158\u003c\/p\u003e \u003cp\u003e8.5 Resource Allocation for 5G Ultra-Dense Networks 160\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 162\u003c\/p\u003e \u003cp\u003eReferences 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Next-Gen Networked Healthcare: Requirements and Challenges 165\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKanica Sachdev and Brejesh Lall\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 165\u003c\/p\u003e \u003cp\u003e9.2 Applications 166\u003c\/p\u003e \u003cp\u003e9.2.1 Remote Robotic-Assisted Surgery 167\u003c\/p\u003e \u003cp\u003e9.2.2 Remote Diagnosis and Teleconsultation 167\u003c\/p\u003e \u003cp\u003e9.2.3 In-Ambulance Treatment 168\u003c\/p\u003e \u003cp\u003e9.2.4 Remote Patient Monitoring 169\u003c\/p\u003e \u003cp\u003e9.2.5 Medical Big Data Management 170\u003c\/p\u003e \u003cp\u003e9.2.6 Augmented Reality (AR) and Virtual Reality (VR) 170\u003c\/p\u003e \u003cp\u003e9.2.7 Emergency Response Strategies 171\u003c\/p\u003e \u003cp\u003e9.3 Technological Prerequisites 172\u003c\/p\u003e \u003cp\u003e9.4 Challenges in 5G Integration in Healthcare 175\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 177\u003c\/p\u003e \u003cp\u003eReferences 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Dynamic Resource Orchestration for Computing, Data, and IoT in Networked Systems: A Data-Centric Approach 185\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSuresh Limkar, Mohammad Alamgir Hossain, Sherif Tawfik Amin, and Yasir Ahmad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 185\u003c\/p\u003e \u003cp\u003e10.1.1 Motivation 187\u003c\/p\u003e \u003cp\u003e10.1.2 Objectives 187\u003c\/p\u003e \u003cp\u003e10.2 Dynamic Resource Orchestration: Foundations 187\u003c\/p\u003e \u003cp\u003e10.2.1 Resource Orchestration Concepts 187\u003c\/p\u003e \u003cp\u003e10.2.2 Dynamic Resource Orchestration’s Evolution 188\u003c\/p\u003e \u003cp\u003e10.2.3 Importance of a Data-Centric Perspective 188\u003c\/p\u003e \u003cp\u003e10.3 Computing in Networked Systems 189\u003c\/p\u003e \u003cp\u003e10.3.1 Cloud Computing Paradigm 189\u003c\/p\u003e \u003cp\u003e10.3.2 Edge Computing and Fog Computing 191\u003c\/p\u003e \u003cp\u003e10.3.3 Integration of Computing Resources 192\u003c\/p\u003e \u003cp\u003e10.4 Data-Centric Orchestration 193\u003c\/p\u003e \u003cp\u003e10.4.1 Data-Driven Resource Allocation 193\u003c\/p\u003e \u003cp\u003e10.4.1.1 Data-Driven Decision-Making 193\u003c\/p\u003e \u003cp\u003e10.4.1.2 Dynamic Scaling 194\u003c\/p\u003e \u003cp\u003e10.4.1.3 Perceptive Formulas 194\u003c\/p\u003e \u003cp\u003e10.4.1.4 Customization and Adaptability 194\u003c\/p\u003e \u003cp\u003e10.4.2 Data Processing and Management 194\u003c\/p\u003e \u003cp\u003e10.4.2.1 Data Locality and Optimization 194\u003c\/p\u003e \u003cp\u003e10.4.2.2 Techniques for Data Movement 194\u003c\/p\u003e \u003cp\u003e10.4.2.3 Data Lifecycle Management 194\u003c\/p\u003e \u003cp\u003e10.4.2.4 AI and Data Analytics Integration 195\u003c\/p\u003e \u003cp\u003e10.4.3 Security and Privacy Considerations 195\u003c\/p\u003e \u003cp\u003e10.4.3.1 Completely Encryption 195\u003c\/p\u003e \u003cp\u003e10.4.3.2 Identity and Access Management 195\u003c\/p\u003e \u003cp\u003e10.4.3.3 Safe Data Processing 195\u003c\/p\u003e \u003cp\u003e10.4.3.4 Regulatory Standard Compliance 195\u003c\/p\u003e \u003cp\u003e10.4.3.5 Privacy-Preserving Techniques 195\u003c\/p\u003e \u003cp\u003e10.4.3.6 Audit Trails and Monitoring 196\u003c\/p\u003e \u003cp\u003e10.5 IoT Integration 196\u003c\/p\u003e \u003cp\u003e10.5.1 Overview of IoT Architecture 196\u003c\/p\u003e \u003cp\u003e10.5.2 IoT Resource Orchestration Challenges 197\u003c\/p\u003e \u003cp\u003e10.5.2.1 Device Heterogeneity 197\u003c\/p\u003e \u003cp\u003e10.5.2.2 Scalability and Data Volume 197\u003c\/p\u003e \u003cp\u003e10.5.2.3 Low-Latency and Real-Time Processing 197\u003c\/p\u003e \u003cp\u003e10.5.2.4 Compatibility and Standards 197\u003c\/p\u003e \u003cp\u003e10.5.3 Combining Data and Computing 197\u003c\/p\u003e \u003cp\u003e10.5.3.1 Data-Centric Orchestration 198\u003c\/p\u003e \u003cp\u003e10.5.3.2 IoT with Machine Learning and AI 198\u003c\/p\u003e \u003cp\u003e10.5.3.3 Dynamic Resource Allocation 198\u003c\/p\u003e \u003cp\u003e10.5.3.4 IoT Security Measures 199\u003c\/p\u003e \u003cp\u003e10.6 Methodologies for Dynamic Resource Orchestration 200\u003c\/p\u003e \u003cp\u003e10.6.1 Methods of Machine Learning 200\u003c\/p\u003e \u003cp\u003e10.6.1.1 Overview of Machine Learning for Resource Management 200\u003c\/p\u003e \u003cp\u003e10.6.1.2 Predictive Resource 200\u003c\/p\u003e \u003cp\u003e10.6.1.3 Fault Prediction and Anomaly Detection 200\u003c\/p\u003e \u003cp\u003e10.6.2 Methods of Optimisation 201\u003c\/p\u003e \u003cp\u003e10.6.2.1 Introducing Resource Orchestration’s Optimisation Techniques 201\u003c\/p\u003e \u003cp\u003e10.6.3 Hybrid Models 201\u003c\/p\u003e \u003cp\u003e10.6.3.1 Optimisation Through Machine Learning Hybrids 201\u003c\/p\u003e \u003cp\u003e10.6.3.2 Combining Rule-Based and Learning-Based Methods: Advancing Hybrid Approaches 201\u003c\/p\u003e \u003cp\u003e10.6.3.3 Continual Enhancement Through Responsive Feedback Mechanisms 202\u003c\/p\u003e \u003cp\u003e10.6.3.4 Harnessing the Power of Adaptive Model Switching 202\u003c\/p\u003e \u003cp\u003e10.7 Case Studies 202\u003c\/p\u003e \u003cp\u003e10.7.1 Practical Applications 202\u003c\/p\u003e \u003cp\u003e10.7.1.1 Aws 202\u003c\/p\u003e \u003cp\u003e10.7.1.2 Autoscaling of Kubernetes Horizontal Pods 202\u003c\/p\u003e \u003cp\u003e10.7.2 Achievements and Insights Acquired 203\u003c\/p\u003e \u003cp\u003e10.7.2.1 Netflix: Using Machine Learning to Deliver Content 203\u003c\/p\u003e \u003cp\u003e10.7.2.2 Google’s Expansion of Kubernetes: Enhancing Scalability 203\u003c\/p\u003e \u003cp\u003e10.7.2.3 Achieving Dynamic Scalability with AWS Auto Scaling: An Airbnb Success Story 203\u003c\/p\u003e \u003cp\u003e10.8 Conclusion 204\u003c\/p\u003e \u003cp\u003eReferences 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Cognitive Cellular Networks: Empowering Future Connectivity Through Artificial Intelligence 209\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMohammad Alamgir Hossain, Suresh Limkar, Sherif Tawfik Amin, and Yasir Ahmad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 209\u003c\/p\u003e \u003cp\u003e11.1.1 Background 209\u003c\/p\u003e \u003cp\u003e11.1.2 Key Objectives of the Chapter 210\u003c\/p\u003e \u003cp\u003e11.2 Foundations of Cognitive Cellular Networks 211\u003c\/p\u003e \u003cp\u003e11.2.1 Architecture of Cellular Networks 211\u003c\/p\u003e \u003cp\u003e11.2.2 Radio Technologies Induced by Cognition 211\u003c\/p\u003e \u003cp\u003e11.2.3 Artificial Intelligence Integration 212\u003c\/p\u003e \u003cp\u003e11.3 AI Algorithms for Network Optimization 213\u003c\/p\u003e \u003cp\u003e11.3.1 Machine Learning Models for Predictive Analysis 213\u003c\/p\u003e \u003cp\u003e11.3.1.1 Machine Learning in Resource Allocation 213\u003c\/p\u003e \u003cp\u003e11.3.1.2 Predictive Analytics for Traffic Management 213\u003c\/p\u003e \u003cp\u003e11.3.1.3 Reinforcement Learning for Self-Optimizing Networks 213\u003c\/p\u003e \u003cp\u003e11.3.1.4 Anomaly Detection to Strengthen Security 214\u003c\/p\u003e \u003cp\u003e11.3.1.5 Artificial Neural Networks for Dynamic Optimization 214\u003c\/p\u003e \u003cp\u003e11.3.1.6 Combining Genetic Algorithms with Cross-Layer Optimization 214\u003c\/p\u003e \u003cp\u003e11.3.2 Spectrum Utilization and Management 214\u003c\/p\u003e \u003cp\u003e11.3.2.1 Dynamic Spectrum Access 214\u003c\/p\u003e \u003cp\u003e11.3.2.2 Brain CRT 215\u003c\/p\u003e \u003cp\u003e11.3.2.3 Enhancing Spectrum Management with AI-Powered Solutions to Combat Interference 215\u003c\/p\u003e \u003cp\u003e11.3.2.4 Achieving Regulatory Compliance in Spectrum Sharing 215\u003c\/p\u003e \u003cp\u003e11.4 Reinforcement Learning in Autonomous Network Management 215\u003c\/p\u003e \u003cp\u003e11.4.1 Essential Guidelines for Mastering Reinforcement Learning 216\u003c\/p\u003e \u003cp\u003e11.4.2 Adaptive Decision-Making in Dynamic Environments 217\u003c\/p\u003e \u003cp\u003e11.4.2.1 Time-Based Learning and the Trade-Off Between Exploration and Exploitation 217\u003c\/p\u003e \u003cp\u003e11.4.2.2 Dynamic Approaches to State Representation and Policy Adaptation 218\u003c\/p\u003e \u003cp\u003e11.4.3 Case Studies on Autonomous Network Management 218\u003c\/p\u003e \u003cp\u003e11.5 Applications of Cognitive Cellular Networks 219\u003c\/p\u003e \u003cp\u003e11.5.1 Upgraded Mobile Broadband 220\u003c\/p\u003e \u003cp\u003e11.5.2 Massive Machine-Type Communication 220\u003c\/p\u003e \u003cp\u003e11.5.3 Ultra-reliable Low-Latency Communication 221\u003c\/p\u003e \u003cp\u003e11.5.4 Use Cases and Practical Implementations 221\u003c\/p\u003e \u003cp\u003e11.6 Challenges and Future Directions 222\u003c\/p\u003e \u003cp\u003e11.6.1 Scalability and Standardization 222\u003c\/p\u003e \u003cp\u003e11.6.2 Future Trends in Cognitive Cellular Networks 222\u003c\/p\u003e \u003cp\u003e11.7 Conclusion 223\u003c\/p\u003e \u003cp\u003eReferences 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Enhancing Scalability and Performance in Networked Applications Through Smart Computing Resource Allocation 227\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAraddhana Arvind Deshmukh, Shailesh Pramod Bendale, Sheela Hundekari, Abhijit Chitre, Kirti Wanjale, Amol Dhumane, Garima Chopra, and Shalli Rani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 227\u003c\/p\u003e \u003cp\u003e12.1.1 Scope and Objectives 229\u003c\/p\u003e \u003cp\u003e12.1.2 Objectives 229\u003c\/p\u003e \u003cp\u003e12.1.2.1 Key Goals of This Study 229\u003c\/p\u003e \u003cp\u003e12.2 Foundations of Smart Computing Resource Allocation 230\u003c\/p\u003e \u003cp\u003e12.2.1 Key Concepts in Resource Allocation 232\u003c\/p\u003e \u003cp\u003e12.2.1.1 Dynamic Resource Allocation 232\u003c\/p\u003e \u003cp\u003e12.2.1.2 Artificial Intelligence (AI) in Resource Management 232\u003c\/p\u003e \u003cp\u003e12.2.1.3 Using Real-Time Analytics to Track Performance 232\u003c\/p\u003e \u003cp\u003e12.2.1.4 Scalability and Elasticity Measures 232\u003c\/p\u003e \u003cp\u003e12.2.1.5 Mechanisms of Adaptive Learning 233\u003c\/p\u003e \u003cp\u003e12.2.1.6 Security-Driven Resource Allocation 233\u003c\/p\u003e \u003cp\u003e12.2.2 The Evolution of Scalability and Performance in Networked Applications 233\u003c\/p\u003e \u003cp\u003e12.2.2.1 Conventional Static Resource Allocation 233\u003c\/p\u003e \u003cp\u003e12.2.2.2 The Arise of Scalability Issues 233\u003c\/p\u003e \u003cp\u003e12.2.2.3 The Cloud Paradigm and Dynamic Resource Allocation 234\u003c\/p\u003e \u003cp\u003e12.2.2.4 Using Smart Computing to Allocate Intelligent Resources 234\u003c\/p\u003e \u003cp\u003e12.2.2.5 Real-Time Adaptation and Predictive Scaling 234\u003c\/p\u003e \u003cp\u003e12.2.2.6 Scalability Beyond Traditionally Assigned Limitations 234\u003c\/p\u003e \u003cp\u003e12.2.2.7 Automation and Autonomy’s Role 234\u003c\/p\u003e \u003cp\u003e12.3 Dynamic Resource Allocation Strategies 235\u003c\/p\u003e \u003cp\u003e12.3.1 Static vs. Dynamic Resource Allocation 237\u003c\/p\u003e \u003cp\u003e12.3.1.1 Static Resource Allocation 237\u003c\/p\u003e \u003cp\u003e12.3.1.2 Dynamic Resource Allocation 237\u003c\/p\u003e \u003cp\u003e12.3.2 Adaptive Resource Allocation Algorithms 237\u003c\/p\u003e \u003cp\u003e12.3.3 Machine Learning Approaches in Resource Allocation 238\u003c\/p\u003e \u003cp\u003e12.4 Intelligent Load Balancing Techniques 238\u003c\/p\u003e \u003cp\u003e12.4.1 Load Balancing in Networked Environments 239\u003c\/p\u003e \u003cp\u003e12.4.2 Importance of Load Balancing in Scalability 240\u003c\/p\u003e \u003cp\u003e12.4.2.1 Load Balancing with Machine Learning 240\u003c\/p\u003e \u003cp\u003e12.4.2.2 Adaptive Load Balancing Algorithms 240\u003c\/p\u003e \u003cp\u003e12.5 Real-Time Monitoring and Feedback Mechanisms 241\u003c\/p\u003e \u003cp\u003e12.5.1 Proactive Monitoring for Allocation of Resources 241\u003c\/p\u003e \u003cp\u003e12.5.2 Decision-Making and Feedback Loops 241\u003c\/p\u003e \u003cp\u003e12.5.3 Real-Time Monitoring 242\u003c\/p\u003e \u003cp\u003e12.6 Case Studies and Best Practices 243\u003c\/p\u003e \u003cp\u003e12.6.1 Cloud-Based Resource Allocation 243\u003c\/p\u003e \u003cp\u003e12.6.2 Edge Computing and Resource Optimization 243\u003c\/p\u003e \u003cp\u003e12.6.3 High-Performance Computing (HPC) Environments 244\u003c\/p\u003e \u003cp\u003e12.7 Security and Privacy Considerations 244\u003c\/p\u003e \u003cp\u003e12.7.1 Ensuring Security in Resource Allocation 244\u003c\/p\u003e \u003cp\u003e12.7.1.1 Overview of Security 244\u003c\/p\u003e \u003cp\u003e12.7.2 Privacy Issues with Wise Resource Distribution 245\u003c\/p\u003e \u003cp\u003e12.7.2.1 Overview of Privacy 245\u003c\/p\u003e \u003cp\u003e12.7.3 Balancing Security and Performance 245\u003c\/p\u003e \u003cp\u003e12.7.3.1 Understanding the Art of Balancing Responsibilities 245\u003c\/p\u003e \u003cp\u003e12.8 Future Trends and Emerging Technologies 246\u003c\/p\u003e \u003cp\u003e12.8.1 Resource Allocation and Edge AI 246\u003c\/p\u003e \u003cp\u003e12.8.1.1 Understanding the Basics of Edge AI 246\u003c\/p\u003e \u003cp\u003e12.8.2 Implications for Quantum Computing 246\u003c\/p\u003e \u003cp\u003e12.8.2.1 A Comprehensive Look at the World of Quantum Computing 246\u003c\/p\u003e \u003cp\u003e12.8.3 Allocating Resources with Blockchain 247\u003c\/p\u003e \u003cp\u003e12.8.3.1 Overview of Blockchain 247\u003c\/p\u003e \u003cp\u003e12.9 Conclusion 248\u003c\/p\u003e \u003cp\u003eReferences 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 5G-Enabled Fusion: Navigating the Future Landscape of Cloud Computing, Internet of Things, and Recommender Systems 251\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSheetal Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Basics of Cloud Computing 251\u003c\/p\u003e \u003cp\u003e13.2 Internet of Things 254\u003c\/p\u003e \u003cp\u003e13.3 5G Technology 257\u003c\/p\u003e \u003cp\u003e13.4 Recommender System 258\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 262\u003c\/p\u003e \u003cp\u003eReferences 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Confluence of Cellular IoT and Data Science for Smart Application using 5G 267\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShruti and Shalli Rani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 267\u003c\/p\u003e \u003cp\u003e14.2 Data Science and Cellular IoT 270\u003c\/p\u003e \u003cp\u003e14.3 Research Problems in Data Science for Cellular IoT 272\u003c\/p\u003e \u003cp\u003e14.4 Sensors in Cellular IoT Smart Farming 273\u003c\/p\u003e \u003cp\u003e14.5 Related Work 275\u003c\/p\u003e \u003cp\u003e14.6 Data Science for Agriculture 277\u003c\/p\u003e \u003cp\u003e14.7 Challenges Faced by Cellular IoT Application in Data Science 278\u003c\/p\u003e \u003cp\u003e14.8 Proposed Model and its Discussion 280\u003c\/p\u003e \u003cp\u003e14.9 Conclusion 281\u003c\/p\u003e \u003cp\u003eReferences 282\u003c\/p\u003e \u003cp\u003eIndex 285\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eGarima Chopra, PhD,\u003c\/b\u003e is an Assistant Professor with Chitkara University Institute of Engineering \u0026amp; Technology at Chitkara University, Punjab, India. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eSuhaib Ahmed, PhD,\u003c\/b\u003e is an Assistant Professor with Model Institute of Engineering and Technology, Jammu, J\u0026amp;K, India. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eShalli Rani, PhD,\u003c\/b\u003e is a Professor with Chitkara University Institute of Engineering \u0026amp; Technology at Chitkara University, Punjab, India.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eComprehensive reference on the latest trends, solutions, challenges, and future directions of 5G communications and beyond\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eCurrent and Future Cellular Systems: Technologies, Applications, and Challenges\u003c\/i\u003e covers the state of the art in architectures and solutions for 5G wireless communication and beyond. This book is unique because instead of focusing on singular topics, it considers various technologies being used in conjunction with 5G and beyond 5G technologies. All new and emerging technologies are covered, along with their problems and how quality of service (QoS) can be improved with respect to future requirements. \u003c\/p\u003e\u003cp\u003eThis book highlights the latest trends in resource allocation techniques due to different device (or user) characteristics, provides a special focus on wide bandwidth millimeter wave communications including circuitry, antennas, and propagation, and discusses the involvement of decision-making processes assisted by artificial intelligence\/machine learning (AI\/ML) in applications such as resource allocation, power allocation, QoS improvement, and autonomous vehicles. Readers will also learn to develop mathematical modeling, perform simulation setup, and configure parameters related to simulations. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eCurrent and Future Cellular Systems\u003c\/i\u003e includes information on: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eThe Internet of Vehicles (IoV), covering requirements, challenges, and limitations of Cellular Vehicle-to-Everything (C-V2X) with Resource Allocation (RA) techniques\u003c\/li\u003e\n\u003cli\u003eIntelligent reflecting surfaces, unmanned aerial vehicles, power optimized frameworks, challenges in a sub-6 GHz band, and communication in a THz band\u003c\/li\u003e\n\u003cli\u003eThe role of IoT in healthcare, agriculture, smart home applications, networking requirements, and the metaverse\u003c\/li\u003e\n\u003cli\u003eQuantum computing, cloud computing, spectrum sharing methods, and performance analysis of WiFi 6\/7 for indoor and outdoor environments\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eProviding expansive yet accessible coverage of the subject by exploring both basic and advanced topics, \u003ci\u003eCurrent and Future Cellular Systems\u003c\/i\u003e serves as an excellent introduction to the fundamentals of 5G and its applications for graduate students, researchers, and industry professionals in the field of wireless communication technologies.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989015838949,"sku":"NP9781394256044","price":135.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394256044.jpg?v=1761782444","url":"https:\/\/k12savings.com\/es\/products\/current-and-future-cellular-systems-isbn-9781394256044","provider":"K12savings","version":"1.0","type":"link"}