دانلود کتاب Future Trends in 5G and 6G: Challenges, Architecture, and Applications

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Cover\nHalf Title\nTitle Page\nCopyright Page\nContents\nPreface\nEditors\nContributors\n1. An Organized Study of Congestion Control Approaches in Wireless Sensor Networks\n 1.1. Introduction\n 1.1.1. Types of Applications [3]\n 1.1.2. Types of Congestion\n 1.2. Related Review Study\n 1.3. Congestion Mitigation Phases in WSN\n 1.3.1. Congestion Awareness Phase\n 1.3.2. Congestion Alertness Phase\n 1.3.3. Congestion Alleviation Phase\n 1.3.3.1. Traffic Rate Control-Based Congestion Control\n AWF\n CcEbH\n PPI\n PSO-Based Routing Protocol\n AFRC\n ALACCP\n GA-SVM\n ACSRO\n DRCDC\n Water Wave Optimization Algorithm\n OFES\n Congestion Control Protocol\n Congestion Control Predictor Model\n 1.3.3.2. Resource Control-Based Congestion Control\n CCR\n Protocol for Controlling Congestion\n CDTMRLB\n DACC\n CAOR\n Water Wave Algorithm\n Hybrid Multi-Objective Optimization Algorithm (PSOGSA)\n FCC\n CCP\n HBCC\n ERRP\n Mobile CC\n ACC-CSMA\n RDML\n 1.3.3.3. Queue-Assisted Congestion Control\n Fuzzy CC Based on AQM\n DRED-FDNNPID\n MAQD\n PPSPC with CICADA\n EDS-MAC\n 1.3.3.4. Priority-Based Congestion Control\n FACC\n PFRC\n Priority-Based Queuing and Transmission Rate Control\n FPBCC\n 1.4. Performance Metrics\n 1.5. Conclusions\n References\n2. NR: Architecture, Protocol, Challenges, and Applications\n 2.1. Introduction\n 2.2. Architecture\n 2.2.1. New-Generation Radio Access Network\n 2.2.2. Introduction to 5G System Architecture\n 2.2.3. Network Slicing in 5G\n 2.2.4. 5G Network Deployment Option\n 2.3. Protocol Stack Model of UE and Core Network\n 2.3.1. 5G Control Plane\n 2.3.1.1. 5G Non-Access Control Protocol\n 2.3.1.1.1. 5GMM Sublayer Introduction\n 32..1.1.1.1. 5GMM Procedures\n 2.3.1.1.1.2. 5GMM States\n 2.3.1.1.2. 5GSM Sublayer Introduction\n 2.3.1.1.2.1. ] 5GSM Procedures\n 2.3.1.1.2.2. 5GSM Sublayer States\n 2.3.1.2. Radio Resource Controller\n 2.3.1.2.1. The RRC Services\n 2.3.1.2.2. The RRC Functions\n 2.3.1.2.3. The RRC States and Procedures\n 2.3.2. 5G User Plane\n 2.3.2.1. Service Data Adaptation Protocol\n 2.3.2.1.1. The SDAP functions\n 2.3.2.1.2. The SDAP Procedures\n 2.3.2.2. Packet Data Convergence Protocol\n 2.3.2.2.2. The PDCP Functions\n 2.3.2.2.1. The PDCP Procedures\n 2.3.2.3. Radio Link Control\n 2.3.2.3.1. Introduction of RLC Entity\n 2.3.2.3.2. The RLC Functions\n 2.3.2.4. Medium Access Control\n 2.3.2.4.1. The MAC Functions\n 2.3.2.4.2. The MAC Procedures\n 2.3.2.5. Physical Layer\n 2.3.3. 5G Channel Mapping\n 2.3.3.1. DL Channel Mapping\n 2.3.3.2. UL Channel Mapping\n 2.4. Challenges\n 2.4.1. Building Complex and Dense Network\n 2.4.1.1. Mobility\n 2.4.1.2. Resource Management\n 2.4.2. Security Issues and Potential Targets in 5G Networks\n 2.5. Application\n 2.5.1. Vehicle-to-Everything\n 2.5.2. Internet of Things\n 2.5.3. Machine-to-Machine\n 2.6. Summary\n References\n3. Comprehensive Survey on Device-to-Device Communication for Next-Generation Cellular Technology\n 3.1. Introduction\n 5G Key Enabling Technologies\n 3.2. Classification of Bands\n 3.2.1. Inband D2D Communication\n 3.2.1.1. Underlay\n 3.2.1.2. Overlay\n 3.2.2. Outband D2D Communication\n 3.2.2.1. Controlled\n 3.2.2.2. Autonomous\n 3.3. D2D Main Classification\n 3.3.1. D2D Control\n 3.3.2. D2D Coverage\n 3.3.3. D2D Communication Mode\n 3.4. Integrated Features\n 3.4.1. D2D Integrated with Millimeter Wave (mmWave)\n 3.4.2. Internet of Things (IoT)\n 3.4.3. Artificial Intelligence (AI)\n 3.4.4. Handover\n 3.4.5. Hybrid Automatic Repeat Request Operation (HARQ)\n 3.4.6. Ultra Dense Network (UDN)\n 3.4.7. Cooperative Communication\n 3.5. Research Challenges\n 3.5.1. Peer Discovery\n 3.5.2. Resource Allocation (RA)\n 3.5.3. Mode Selection\n 3.5.3.1. Pure Cellular Mode\n 3.5.3.2. Partial Cellular Mode\n 3.5.3.3. Dedicated Mode\n 3.5.3.4. Underlay Mode\n 3.5.4. Interference Management (IM)\n 3.5.5. Power Control\n 3.6. Conclusion\n References\n4. Challenges, Opportunities, and Applications of 5G Network\n 4.1. Introduction\n 4.2. 5G Technology: Overall Architecture\n 4.2.1. Network Slicing\n 4.3. 5G Enhanced Overall System Architecture\n 4.3.1. Overall Network Slicing\n 4.3.2. End-to-End (E2E) Service Operations—Lifecycle Management\n 4.3.3. Overall RAN Architecture\n 4.3.4. Core and Transport Architecture\n 4.3.4.1. Core Network Architecture\n 4.3.4.2. Transport Network Infrastructure\n 4.4. Threats and Challenges\n 4.4.1. 5G Threat Landscape\n 4.4.1.1. Threat Landscape of Internet of Things (IoT)\n 4.4.1.2. Security Issues Related to SDN/SDMN\n 4.4.1.3. Network Function Virtualization (NFV)-Related Security Issues\n 4.4.1.4. MEC and Cloud-Related Security Issues\n 4.4.1.5. Network Slicing-Related Security Issues\n 4.4.2. 5G Security Aspects\n 4.4.3. Security Challenges and Opportunities Related to 5G Communication\n 4.5. Privacy Perspective\n 4.6. Applications of 5G\n 4.7 Conclusion\n References\n5. Machine Learning and Deep Learning for Intelligent and Smart Applications\n 5.1. Introduction\n 5.2. Motivation\n 5.3. Need for Machine Learning\n 5.4. Machine Learning (ML)—Definitions\n 5.5. Machine Learning—Process\n 5.6. ML Techniques\n 5.6.1. Supervised and Unsupervised Techniques\n 5.7. Applications of Machine Learning\n 5.8. Applications of Deep Learning\n 5.8.1. Self-Driving Cars\n 5.8.2. Fraud News and Aggregation News Detection\n 5.8.3. Natural Language Processing (NLP)\n 5.8.4. Virtual (Tacit) Assistants\n 5.8.5. Entertainment\n 5.8.6. Visual—Recognition\n 5.8.7. Fraud Detection\n 5.8.8. Healthcare\n 5.8.9. Personalization\n 5.8.10. Detecting Developmental Delay in Children\n 5.8.11. Black and White Image Colorization\n 5.8.12. Adding Sounds to Silent Movies\n 5.8.13. Machine Translation that Is Automatic\n 5.8.14. Handwriting Generation by Machine\n 5.8.15. Playing Games Automatically\n 5.8.16. Image-Language Translations\n 5.8.17. Restoration of Pixel\n 5.8.18. Photo Descriptions\n 5.8.19. Deep Dreaming\n 5.8.20. Demographic and Election Predictions\n Summary\n References\n6. Key Parameters in 5G for Optimized Performance\n 6.1. Introduction\n Why Do We Need 5G?\n 6.2. 5G Systems\n 6.2.1. Overview of 5G System\n 6.2.2. 5G Specifications\n 6.2.3. 5G Enabling Technologies\n 6.3. Network Optimization Parameters\n 6.3.1. High Data Rates\n 6.3.2. Latency\n 6.3.3. Capacity\n 6.3.4. Energy Efficiency\n 6.3.5. Mobility\n 6.4. Latency\n 6.4.1. Physical Layer Design\n 6.4.2. Transport Layer Design\n 6.4.2.1. Mobile Edge Computing\n 6.4.3. Network Layer Design\n 6.5. Energy Efficiency\n 6.5.1. Energy Efficiency at Base Station Level\n 6.5.2. Energy Efficiency with Cached Technique\n 6.5.3. Energy Efficiency by Resource Allocation\n 6.5.4. Energy Efficiency by Machine Learning Techniques\n 6.6. Network Capacity\n 6.6.1. Spectrum Efficiency\n 6.6.2. Spectrum Extension\n 6.6.3. Connection Density\n 6.7. Network Softwarization\n 6.7.1. Software Defined Networking (SDN)\n 6.7.2. Network Function Virtualization (NFV)\n 6.7.3. Network Slicing\n 6.7.3.1. Concept of Network Slicing\n 6.8. Network Slicing Enabling Technologies\n 6.8.1. SDN/NFV\n 6.8.2. RAN Slicing\n 6.8.3. Fog Radio Access Networks\n 6.8.4. Deep Learning\n 6.9. Open Issues and Challenges\n 6.9.1. Latency\n 6.9.2. Energy Efficiency\n 6.9.3. Network Capacity\n 6.9.4. Network Slicing\n References\n7. Applications of Machine Learning in Wireless Communication: 5G and Beyond\n Introduction\n 7.1. The Evolution of Cellular Network Technologies\n 7.1.1. 1G\n 7.1.2. 2G\n 7.1.3. 3G\n 7.1.4. 4G\n 7.1.5. 5G\n 7.1.6. 6G\n 7.2. 5G: At a Glance\n 7.3. How to Realize 5G?\n 7.4. 6G: Vision\n 7.4.1. Research Challenges in 6G\n 7.5. Moving from 5G to 6G\n 7.6. AI in Wireless Communication\n 7.6.1. Introduction to AI and ML\n 7.6.1.1. AI\n 7.6.1.2. ML\n 7.6.2. AI-Enabled Wireless Communication\n 7.6.2.1. Big Data Analytics\n 7.6.2.2. Intelligence in Wireless Communication\n 7.7. ML Model for Wireless Communication\n 7.7.1. Key Steps in ML\n 7.7.1.1. Training Data\n 7.7.1.2. Feature Extraction\n 7.7.1.3. Learning Function and Prediction\n 7.7.2. ML for Wireless Communication\n 7.7.2.1. Physical Layer with ML\n 7.7.2.1.1. Channel Coding and Modulation\n 7.7.2.1.2. Channel Estimation\n 7.7.2.1.3. Synchronization\n 7.7.2.1.4. Positioning\n 7.7.2.1.5. Beamforming\n 7.7.2.2. Medium Access Control Layer with ML\n 7.7.2.3. Application Layer with ML\n 7.7.3. The applications of ML in wireless communication\n 7.8. Types of ML Categories for Wireless Communication\n 7.8.1. Supervised Learning\n 7.8.2. Unsupervised Learning\n 7.8.3. Transfer Learning\n 7.8.4. Reinforcement Learning\n 7.9. Deep Learning\n 7.10. Conclusion\n References\n8. GREEN-Cloud Computing (G-CC) Data Center and Its Architecture toward Efficient Usage of Energy\n 8.1. Introduction\n 8.1.1. The Need for GCC\n 8.2. History of Cloud Computing\n 8.3. Overview of Green Cloud Computing\n 8.3.1. Features of Green Cloud Computing\n 8.3.2. Approaches\n 8.3.2.1. Dynamic Provisioning\n 8.3.2.2. Multi-Occupancy\n 8.3.2.3. Server Utility\n 8.3.2.4. Efficiency of Data Center\n 8.4. Benefits of Going Green\n 8.4.1. Decrease in Green House Gases Emission (GHG-E)\n 8.4.2. Dematerialization\n 8.4.3. Move to Renewable Energy Sources\n 8.4.4. Redesign of Cooling Framework\n 8.4.5. Risk Management\n 8.4.6. Improved Social and Corporate Image\n 8.5. Recent Trends in Green Cloud Computing\n 8.5.1. The Emergence of Digital Natives in the Workforce\n 8.5.2. Artificial Intelligence (AI)\n 8.5.3. Hybrid Cloud Computing\n 8.5.4. Quantum Computing\n 8.5.5. High Performance Computing in Public Cloud Storage\n 8.6. Techniques or Methods to Make Cloud Green\n 8.7. Green Cloud Computing Architecture\n 8.8. Green Data Center Architecture\n 8.9. Energy to be Saved\n 8.10. Optimization of Energy Efficiency\n 8.10.1. Improvement Techniques in Performance-Energy-Temperature Aware Computing\n 8.10.2. Data Resource Tier Optimization\n 8.10.3. Exceptionally Productivity Data Center Plan\n 8.10.4. Creating Green Maturity Model\n 8.10.5. Green Software\n 8.10.6. Wireless Sensor Network for Data Center Cooling\n 8.11. Challenges in Implementation and Future Scope\n 8.11.1. Energy-Aware Dynamic Resource Allocation\n 8.11.2. QoS-Based Resource Selection and Provisioning\n 8.11.3. Effective Consolidation of VMs for Managing Heterogenous Workloads\n 8.11.4. Advancements in Virtual Network Topologies\n 8.11.5. Security\n 8.12. Conclusion\n References\n9. SDR Network & Network Function Virtualization for 5G Green Communication (5G-GC)\n 9.1. Introduction\n 9.2. 5G—Green Network\n 9.2.1. Classification of 5G Green Communication Network\n 9.2.1.1. Off-Grid Base Stations (OFF-GBS)\n 9.2.1.2. On-Grid Base Stations (ON-GBS)\n 9.3. SDR toward 5G\n 9.4. SDR Networking\n 9.4.1. Architecture\n 9.4.2. Design\n 9.5. SDN\n 9.5.1. Architecture\n 9.5.2. Design\n 9.6. Hybrid Architecture of SDR and SDR\n 9.7. Network Function Virtualization (NFV)\n 9.7.1. Architecture\n 9.7.2. Design\n 9.8. Current Standardization for SDR, SDN & NFV Enabled\n 9.8.1. SDR\n 9.8.2. SDN\n 9.8.3. NFV\n 9.9. Integration of SDN with NFV\n 9.9.1. Migration of Network\n 9.9.2. Network Monitoring\n 9.9.3. Service & Optimization\n 9.9.4. Cost Reduction\n 9.9.5. Requirements\n 9.10. Network Management & Operation in 5G Environment\n 9.11. Securing NFV-Based SDN\n 9.11.1. Client Plane Policy\n 9.11.2. Security Orchestration plane\n 9.11.3. Security Enforcement Plane\n 9.12. Challenges\n 9.12.1. Virtualization for Infrastructure Abstraction\n 9.12.2. Virtual Network Construction\n 9.12.3. Service Quality Assurance in Virtual Network Environments\n 9.12.4. Energy-Aware Network Design\n 9.12.5. Millimeter-Wave\n 9.12.6. Massive MIMO\n 9.12.7. Heterogeneous Networks\n 9.12.8. C-RAN\n 9.13. Conclusion\n References\n10. An Intensive Study of Dual Patch Antennas with Improved Isolation for 5G Mobile Communication Systems\n 10.1. Introduction\n 10.2. Antenna Configuration and Design Concept\n 10.2.1. Single Antenna Design\n 10.2.2. Design of a 2 x 2 Antenna with λ/4 Distance\n 10.2.3. Design of 2 x 2 Antenna with λ/3 Distance\n 10.2.4. Design of 2 x 2 Antenna with λ/2 Spacing\n 10.2.5. Electric Field Distribution\n 10.2.6. Envelope Correlation Coefficient (ECC) and Diversity Gain (DG)\n 10.3. Conclusion\n References\n11. Design of Improved Quadruple-Mode Bandpass Filter Using Cavity Resonator for 5G Mid-Band Applications\n 11.1. Introduction\n 11.1.1. Cavity Filters\n 11.2. Filter Structure\n 11.2.1. Cavity Resonator\n 11.3. Quadruple-Mode Resonator\n 11.3.1. Configurations and Characteristics\n 11.4. Design of Cavity Filter\n 11.4.1. Normalized Capacitance between Resonators and Ground\n 11.4.2. Physical Filter Dimensions\n 11.5. Coaxial Resonator Cavity Filters\n 11.6. Results and Discussions\n 11.7. Conclusion\n References\n12. Wavelet Transform for OFDM-IM under Hardware Impairments Performance Enhancement\n 12.1. Introduction\n 12.2. System Model\n 12.2.1. Conventional OFDM-IM\n 12.2.2. WOFDM-IM\n 12.2.3. Hardware Impairments\n 12.3. Performance Analysis of WOFDM-IM\\OFDM-IM under Hardware Impairments\n 12.4. Numerical Results\n 12.5. Conclusion\n References\n13. A Systematic Review of 5G Opportunities, Architecture and Challenges\n 13.1. Introduction\n 13.2. 5G Opportunities\n 13.3. Enhanced Mobile Broadband\n 13.4. Ultra-reliable communications\n 13.5. Slicing of network\n 13.6. Natural Disaster\n 13.7. Product Manufacturing Industry\n 13.8. Construction Industry\n 13.9. Street Transportation\n 13.10. Health Care Industries\n 13.11. Smart Cities and Communities\n 13.12. Education\n 13.13. Tourism\n 13.14. Agriculture\n 13.15. Finance\n 13.16. Virtual Office\n 13.17. Holographic communication and Haptic feedback []\n 13.18. Integrated Access Backhaul []\n 13.19. 5G Architecture\n 13.20. Non-Standalone Architecture, option 3x\n 13.21. Non-Standalone Architecture, option 3a\n 13.22. Non-Standalone Architecture, option 3\n 13.23. Standalone - Architecture, option 2\n 13.24. Standalone Architecture, option 5\n 13.25. Non-Standalone Architecture, option 4\n 13.26. Non-Standalone Architecture, option 4a\n 13.27. Non-Standalone Architecture, Option 7a\n 13.28. Non-Standalone Architecture, option 7\n 13.29. 5G Challenges\n 13.30. Complex hardware design and architecture\n 13.31. Conclusion\n References\n14. The Latest 6G Artificial Intelligence Network Applications\n 14.1. Introduction\n 14.2. The 6G Satellite Communication Systems\n 14.3. The Vision in Combination of Artificial Intelligence with 6G and its Expectations\n 14.4. Trends and Technology Applications of 6G Networks\n 14.5. The Need for 6G Networking System\n 14.6. Requirement of 6G Application in Healthcare System\n 14.7. 6G Intelligent Connectivity\n 14.8. 6G Communication Smart Society\n 14.9. 6G Automated Communication Systems\n 14.10. 6G Implementation in Industrial Sector\n 14.11. Future Perspectives for 6G networking systems\n 14.12. Conclusion\n References\n15. A Review of Artificial Intelligence Techniques for 6G Communications: Architecture, Security, and Potential Solutions\n 15.1. Introduction\n 15.2. What Is 6G and Its Requirements?\n 15.3. 6G Anticipations\n 15.4. Integrating AI with 6G\n 15.5. Self-Driving Vehicles: A Use Case\n 15.6. A 6G Vision\n 15.6.1. Holographic Communications\n 15.6.2. Tactile Communications\n 15.6.3. Communications Based on Human Bond\n 15.7. Challenges and Their Potential Solutions\n 15.7.1. High Intelligence versus Complexity\n 15.7.2. Security versus Spectral Efficiency\n 15.7.3. Potential Health Issues\n 15.7.4. Fundamentals of 3D Network Latency and Reliability\n 15.8. Conclusion\n References\n16. Layered Architecture and Issues in 6G\n Introduction\n 5G Wireless Networks\n Basic Architecture of 5G Network\n 6G Wireless Networks\n Issues in 6G\n Network Security\n Scalability\n Network Coverage Area\n Network Component Costs\n Artificial Intelligence\n Database Management\n Protocol Integration\n Computational Cost and Over Expectation from 5G\n Conclusion\n References\n17. Artificial Intelligence Techniques for 6G\n 17.1. Introduction\n 17.2. What Exactly Is 6G?\n 17.3. The Architecture of 6G Networks\n 17.4. AI-Based Technologies for 6G\n 17.4.1. Supervised Learning\n 17.4.2. Unsupervised Learning\n 17.4.3. Reinforcement Learning (RL)\n 17.5. Hardware-Algorithm Co-Design\n 17.6. Conclusion\n References\n18. Antenna Array Design for Massive MIMO System in 5G Application\n 18.1. Introduction\n 18.2. Journey of Massive MIMO around the Globe\n 18.3. Importance of a Good Antenna Design\n 18.4. Literature Review\n 18.5. Wearable Antenna in MIMO Technology\n 18.6. Specific Absorption Rate\n 18.7. Significance of Antenna Array\n 18.8. Antenna Design\n 18.9. Results\n 18.10. Conclusion\n References\nIndex