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Electric vehicle design : design, simulation and applications / / edited by Krishan Arora, Suman Lata Tripathi and Himanshu Sharma
Electric vehicle design : design, simulation and applications / / edited by Krishan Arora, Suman Lata Tripathi and Himanshu Sharma
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc.
Descrizione fisica 1 online resource (358 pages)
Disciplina 629.22/93
Altri autori (Persone) TripathiSuman Lata
SharmaHimanshu
Soggetto topico Electric vehicles - Design and construction
ISBN 9781394205080
1394205082
9781394205097
1394205090
9781394205073
1394205074
9781394204373
139420437X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Development of Braking Systems in Fuel Cell Electric Vehicles -- 1.1 Introduction -- 1.2 Historical Background of Fuel Cell -- 1.3 ADVISOR -- 1.4 Why Hydrogen is Preferred -- 1.5 What is a Fuel Cell? -- 1.6 Working of Fuel Cells -- 1.7 Types of Fuel Cells -- 1.7.1 Direct Methanol Fuel Cell (DMFC) -- 1.7.2 Phosphoric Acid Fuel Cell (PAFC) -- 1.7.3 Alkaline Fuel Cell (AFC) -- 1.7.4 Solid Oxide Fuel Cell (SOFC) -- 1.7.5 Molten Carbonate Fuel Cell (MCFC) -- 1.8 Block Diagram of Vehicle on MATLAB/Simulink -- 1.9 Braking System in Vehicle -- 1.10 Regenerative Braking System -- 1.11 Anti-Lock Braking System (ABS) -- 1.11.1 Component of ABS -- 1.11.1.1 Wheel Speed Sensor -- 1.11.1.2 Valves -- 1.11.1.3 Pumps -- 1.11.1.4 Electronic Control Unit -- 1.11.2 Types of the ABS Model -- 1.11.3 Anti-Lock Braking System Plot -- 1.12 Conclusion -- References -- Chapter 2 Design and Applications of Fuel Cells -- 2.1 Introduction -- 2.2 Types of Electric Vehicles -- 2.2.1 Battery Electric Vehicles (BEVs) -- 2.2.2 Hybrid Electric Vehicles (HEVs) -- 2.2.3 Plug-In Hybrid Electric Vehicles (PHEVs) -- 2.2.4 Fuel Cell Electric Vehicles (FCEVs) -- 2.3 Design Equations of Fuel Cells -- 2.3.1 Nernst Equation -- 2.3.2 Ohm's Law -- 2.3.3 Power Output -- 2.3.4 Efficiency Equation -- 2.3.5 Kinetic Current Density Equation -- 2.3.6 Over-Potential Equation -- 2.3.7 Heat Generation Equation -- 2.4 Designing of Fuel Cells -- 2.5 Types of Fuel Cells -- 2.6 Solid Oxide FCs (SOFCs) -- 2.6.1 Working of SOFCs -- 2.6.2 Advantages of SOFCs -- 2.6.3 Disadvantages of SOFCs -- 2.6.4 Applications of SOFCs -- 2.7 Alkaline Fuel Cells (AFCs) -- 2.7.1 Working of AFCs -- 2.7.2 Advantages of AFCs -- 2.7.3 Disadvantages of AFCs -- 2.7.4 Applications of AFCs -- 2.8 Molten Carbonate Fuel Cell (MCFC) -- 2.8.1 Working of MCFC.
2.8.2 Advantages of MCFCs -- 2.8.3 Disadvantages of MCFCs -- 2.8.4 Applications of MCFCs -- 2.9 Phosphoric Acid Fuel Cells (PAFCs) -- 2.9.1 Working of PAFCs -- 2.9.2 Advantages of PAFCs -- 2.9.3 Disadvantages of PAFCs -- 2.9.4 Applications of PAFCs -- 2.10 Polymer Electrolyte Membrane Fuel Cell (PEMFC) -- 2.10.1 Working of PEMFC -- 2.10.2 Advantages of PEMFCs -- 2.10.3 Advantages of PEMFCs -- 2.10.4 Applications of PEMFCs -- 2.11 Direct Methanol Fuel Cells (DMFCs) -- 2.11.1 Working of DFMC -- 2.11.2 Advantages of DMFCs -- 2.11.3 Disadvantages of DMFCs -- 2.11.4 Applications of DMFCs -- 2.12 Parameters Affecting the Performance of FCs -- References -- Chapter 3 Smart Energy Management and Monitoring System for Electric Vehicles with IoT Integration -- 3.1 Introduction -- 3.2 The Control of Electric Vehicles Using IoT -- 3.2.1 Battery Management System -- 3.2.2 Safe and Intelligent Driving -- 3.2.3 System for Fault Alert and Preventative Maintenance -- 3.2.4 Data from Telemetry -- 3.2.4.1 Battery Usage Information -- 3.2.4.2 Report on Charging -- 3.2.4.3 Notify About Nearby Charging Stations -- 3.2.4.4 Data on Driver Behavior -- 3.3 IoT Management Issues with Electric Vehicles -- 3.3.1 Internet Safety -- 3.3.2 Higher Price -- 3.3.3 Considering the Challenges and Advantages -- 3.4 Monitoring and Management Benefits of IoT -- 3.4.1 IoT and Battery Management Systems -- 3.4.2 IoT for Safe and Intelligent Driving -- 3.4.3 Theft Prevention -- 3.4.4 Detection of Falling or Crashes -- 3.4.5 Battery Leasing Made Simple -- 3.5 Predictive Maintenance System with Fault Alerts -- 3.6 IoT Management and Monitoring Issues with Electric Vehicles -- 3.6.1 Threat from Cyber Attacks -- 3.6.2 Electric Car Prices are Quite High -- 3.6.3 Technological Difficulty -- 3.6.4 Connectivity and Reliance on Power -- 3.6.5 Battery Management and Monitoring System.
3.6.6 Prototype of a Battery Charge Control and Monitoring System -- 3.6.7 Scenario for the Battery Monitoring and Management System -- 3.7 Microcontroller -- 3.7.1 DC Current Sensor -- 3.7.2 Fuel Gauge Module for Li-Lon Batteries -- 3.8 IoT-Based Systems for Battery Management and Monitoring -- 3.9 Design of Battery Charge Control and Monitoring System -- 3.10 Results and Discussion -- 3.11 Conclusions -- 3.12 Future Scope of IoT in Electric Vehicles -- References -- Chapter 4 A Review of Electric Vehicles: Technologies and Challenges -- 4.1 Introduction -- 4.2 Electric Motors -- 4.2.1 DC Series Motor -- 4.2.2 Brushless DC Motors -- 4.2.2.1 Out-Runner-Type BLDC Motor -- 4.2.2.2 In-Runner-Type BLDC Motor -- 4.2.3 Permanent Magnet Synchronous Motor -- 4.2.4 Three-Phase AC Induction Motors -- 4.2.5 Switched Reluctance Motors -- 4.3 Power Electronic Converters -- 4.3.1 Bi-Directional DC-DC Converter -- 4.3.1.1 Non-Isolated Converters -- 4.3.1.2 Isolated Converters -- 4.4 Battery in Electric Vehicles -- 4.4.1 Types of Battery in Electric Vehicles -- 4.4.2 Traditional Battery Charging Approach -- 4.4.2.1 Constant Current (CC) Charging Approach -- 4.4.2.2 Constant Voltage (CV) Charging Approach -- 4.4.2.3 Constant Current-Constant Voltage (CC-CV) Charging Approach -- 4.4.2.4 Multi-Stage Constant Current (MCC) Approach -- 4.5 Conclusion -- References -- Chapter 5 Electric Vehicle and Design Using MATLAB -- List of Abbreviations -- 5.1 Introduction -- 5.2 Motivation -- 5.2.1 History of EVs -- 5.3 Basic Fundamentals of EVs -- 5.4 Why Electric Vehicles? -- 5.5 Comparison Between ICV and EV -- 5.6 Classification of EVs -- 5.7 Design and Structure of EV -- 5.7.1 HEV -- 5.7.2 FCEV -- 5.7.3 PHEV -- 5.7.4 Basic Design of EV -- 5.8 Mathematical Model of an Electric Vehicle -- 5.9 Control Strategy of EVs -- 5.10 Design Methodology for Electric Vehicles (EVs).
5.11 Latest Emerging Technology in EV -- 5.12 Performance Valuation of BLDC Motor and Induction Motor for Electric Vehicle Propulsion Application -- 5.12.1 A Mathematical Model for a Brushless DC (BLDC) Motor-Driven Electric Vehicle -- 5.12.2 Induction Motor -- 5.12.3 Mathematical Model for an Induction Motor-Driven Electric Vehicle -- 5.12.4 Induction Motor Design with Their Specifications -- 5.13 Conclusion -- References -- Chapter 6 Model Order Reduction of Battery for Smart Battery Management System -- 6.1 Introduction -- 6.2 Problem Formulation -- 6.3 Modeling of Battery -- 6.4 Methodology for Model Order Reduction -- 6.5 Result and Discussion -- 6.6 Conclusion -- Appendix -- References -- Chapter 7 Power Electronic Converters for Electric Vehicle Application -- 7.1 Introduction -- 7.2 Types of Electrical Vehicle and Role of Power Electronic Converter -- 7.2.1 Battery Electric Vehicles (BEVs) -- 7.2.1.1 Power Electronics in BEVs -- 7.2.2 Plug-In Hybrid Electric Vehicles (Plug-In HEVs) -- 7.2.2.1 Power Electronics in Plug-In Hybrid Electric Vehicles (Plug-In HEV) -- 7.2.3 Hybrid Electric Vehicles (HEVs) -- 7.2.3.1 Series Hybrid Electric Vehicles (SHEVs) -- 7.2.3.2 Parallel Hybrid Electric Vehicles (PHEVs) -- 7.2.3.3 Series-Parallel Hybrid Electric Vehicles (SPHEVs) -- 7.2.3.4 Power Electronics in Hybrid Electric Vehicles (HEVs) -- 7.2.4 Fuel Cell Electric Vehicles (FCEVs) -- 7.2.4.1 Power Electronics in Fuel Cell Electric Vehicles (FCEVs) -- 7.2.5 Solar Cell Electric Vehicles (SCEVs) -- 7.2.5.1 Power Electronics in Solar Cell Electric Vehicles (SCEVs) -- 7.3 Recent Development in Power Electronic Converter -- 7.4 Power Electronic Converters in Electric, Hybrid, and Fuel Cell Vehicles -- 7.4.1 Power Electronic Converters in EVs -- 7.4.2 Categorization of Power Electronic Converters -- 7.5 Challenges in Power Electronic Vehicular System.
7.5.1 Efficiency -- 7.5.2 Longevity -- 7.5.3 Performance of EV -- 7.5.4 Luxurious Features -- 7.5.5 Safety -- 7.5.6 Overall Cost -- 7.5.7 Noise -- 7.6 Conclusion -- References -- Chapter 8 Integrating Electric Vehicles Into Smart Grids Through Data Analytics: Challenges and Opportunities -- 8.1 Introduction -- 8.2 Smart Grid and Electric Vehicle -- 8.3 Impact of Electric Vehicle-Based Data Analytics for Smart Grids -- 8.4 Importance of Resource Availability, Price, and Load for EV -- 8.5 Electric-Tariff Design Based on Impact of Electric Vehicle Usage -- 8.6 Data Analytics for Electric Vehicles -- 8.7 Machine Learning for EV Analytics -- 8.8 What are the Different ML Algorithms Used by Authors for EV Analytics? -- 8.9 Importance of Data Analysis in the EV Industry Using an Open Source Data -- 8.10 Description of the Dataset -- 8.11 Features and Factors That Influence the Prices of EVs -- 8.12 Price Prediction of EVs -- 8.13 Random Forest-Based Price Prediction of Electric Vehicles -- 8.14 Machine Learning Model -- 8.15 Electric Vehicle Usage in India -- 8.16 The Challenges of Adopting EV in India -- 8.17 How to Increase Renewable Energy in India to Meet EV Demand -- Conclusion -- References -- Chapter 9 Hybrid Electrical Vehicle Designs -- 9.1 Introduction -- 9.2 Plug-In Hybrid Electric Vehicles -- 9.3 Classification of HEVs -- 9.3.1 Series Hybrid -- 9.3.2 Parallel Hybrid -- 9.3.3 Series-Parallel Hybrid -- 9.4 Fuel Cell Electric Vehicles (FCEVs) -- 9.4.1 Micro-Hybrids -- 9.4.2 Mild Hybrids -- 9.4.3 Full Hybrids -- 9.5 Hybrid Electric Vehicle System Design and Analysis -- 9.6 Control Strategy in Series Hybrid Drivetrain Configuration -- 9.6.1 Modes of Operation -- 9.6.2 Max. SoC-of-PPS Control Strategy -- 9.7 Design of Fuel Cell Electric Vehicles with Fuel Economy -- 9.7.1 Traction Mode -- 9.8 Conclusion -- References.
Chapter 10 EV Battery Charging System.
Record Nr. UNINA-9911020337603321
Hoboken, NJ : , : John Wiley & Sons, Inc.
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Energy-Efficient Communication Networks
Energy-Efficient Communication Networks
Autore Chopra Shakti Raj
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (205 pages)
Disciplina 621.382
Altri autori (Persone) AroraKrishan
TripathiSuman Lata
KumarVikram
Soggetto topico Telecommunication - Power supply
ISBN 1-394-27167-0
1-394-27168-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- List of Contributors -- Chapter 1 Efficient Energy Management in Hyperledger Fabric Blockchain Networks: A Proposed Optimized Solution -- 1.1 Introduction -- 1.2 Methodology -- 1.3 Experimental Analysis -- 1.3.1 Existing Problem in the Network -- 1.3.2 Proposed Hyperledger Fabric Network Approach -- 1.4 Results and Discussion -- 1.5 Conclusion -- References -- Chapter 2 Framework for UAV-Based Wireless Power Harvesting -- 2.1 Introduction -- 2.2 Literature Review -- 2.2.1 Proposed Framework -- 2.2.2 Integration with UAV Systems -- 2.2.3 Methodology -- 2.3 Results and Discussion -- 2.4 Conclusion -- References -- Chapter 3 Future Generation Technology and Feasibility Assessment -- 3.1 Introduction -- 3.1.1 Technological Breakthroughs -- 3.1.2 Economic Viability and Feasibility -- 3.1.3 Regulatory Environments -- 3.1.4 Atmospheric Reliability -- 3.1.5 Customer Requirements -- 3.1.6 Societal Acceptability -- 3.2 Next-Generation Electrical Technologies -- 3.2.1 Smart Grids -- 3.2.1.1 Components and Features -- 3.2.1.2 Advantages -- 3.2.1.3 Challenges -- 3.2.2 Renewable Energy Integration -- 3.2.2.1 Grid Integration -- 3.2.2.2 Power Electronics and Control System -- 3.2.2.3 Energy Storage -- 3.2.2.4 Transmission and Distribution -- 3.2.2.5 Challenges -- 3.2.3 Energy Storage -- 3.2.3.1 Types of Energy Storage -- 3.2.3.2 Applications of Energy Storage -- 3.2.3.3 Advancements and Challenges -- 3.2.4 Electric Vehicles -- 3.2.4.1 Types of Electric Vehicles -- 3.2.4.2 Key Components and Systems -- 3.2.4.3 Challenges -- 3.2.5 Power Electronics -- 3.2.5.1 Components and Systems -- 3.2.5.2 Applications -- 3.2.5.3 Challenges and Future Trends -- 3.2.6 Internet of Things (IoT) and Connectivity -- 3.2.6.1 Internet of Things (IoT) -- 3.2.6.2 Connectivity in Electrical Engineering.
3.2.6.3 Advantages and Challenges -- 3.3 Artificial Intelligence -- 3.3.1 Types of Artificial Intelligence -- 3.3.1.1 Type I -- 3.3.1.2 Type II (Based on Functionalities) -- 3.3.2 Applications of AI in Electrical Engineering -- 3.3.2.1 Design and Development -- 3.3.2.2 Predictive Maintenance -- 3.3.2.3 Power System and Grid Management -- 3.3.2.4 Automation and Control Systems -- 3.3.2.5 Energy Efficiency -- 3.4 Machine Learning -- 3.4.1 Types of Machine Learning -- 3.4.1.1 Supervised Machine Learning -- 3.4.1.2 Unsupervised Machine Learning -- 3.4.1.3 Semi-Supervised Learning -- 3.4.1.4 Reinforcement Learning -- 3.4.2 Applications of Machine Learning in Electrical Engineering -- 3.4.2.1 Predictive Maintenance -- 3.4.2.2 Power System Optimization -- 3.4.2.3 Control Systems and Optimization -- 3.4.2.4 Energy Efficiency -- 3.4.2.5 Design and Development -- 3.5 Conclusion -- References -- Chapter 4 IoT-Enabled Weather Forecasting Systems in Future Networks: Constraints and Solutions -- 4.1 Introduction -- 4.2 Need of IoT-Based Weather Forecasting System -- 4.3 Methodology and Results -- 4.4 Conclusion -- References -- Chapter 5 Cognitive Radio-Based NOMA Communication Networks -- 5.1 Introduction to Cognitive Radio and NOMA Networks -- 5.1.1 Motivation for Integrating Cognitive Radio with NOMA -- 5.2 Fundamentals of Cognitive Radio Technology -- 5.2.1 Spectrum Sensing Techniques in Cognitive Radio -- 5.2.2 Dynamic Spectrum Access (DSA) -- 5.2.3 Spectrum Management -- 5.2.4 Cognitive Radio Architectures and Protocols -- 5.3 Principles of Non-Orthogonal Multiple Access (NOMA) -- 5.3.1 Orthogonal Multiple Access versus NOMA -- 5.3.2 NOMA Techniques and Variants -- 5.3.3 Advantages and Challenges of NOMA Networks -- 5.4 Integration of Cognitive Radio with NOMA -- 5.4.1 Cognitive Radio Capabilities and Spectrum Sensing in NOMA Networks.
5.4.2 Spectrum-Sharing Techniques in Cognitive Radio- NOMA Systems -- 5.4.3 Cognitive Radio-NOMA Architecture and Protocol Stack -- 5.4.4 Resource Allocation and Management in Cognitive Radio-NOMA Networks -- 5.4.4.1 Power Allocation and Control Strategies -- 5.4.4.2 Spectrum Sensing and Dynamic Spectrum Access in NOMA-CR Networks -- 5.4.4.3 QoS Provisioning and Optimization Techniques -- 5.5 Performance Evaluation and Analysis -- 5.5.1 Metrics for Assessing Cognitive Radio-NOMA Networks -- 5.5.2 Simulation and Modeling Approaches -- 5.6 Applications and Use Cases -- 5.6.1 Cognitive Radio-NOMA in Next-Generation Wireless Systems -- 5.6.2 Internet of Things (IoT) and Machine-to-Machine (M2M) Communications -- 5.6.3 Vertical Industry Applications -- 5.7 Challenges and Future Directions -- 5.7.1 Interference Management and Coexistence Issues -- 5.7.2 Security and Privacy Concerns in Cognitive Radio- NOMA Systems -- 5.7.3 Emerging Trends and Future Research Directions -- 5.8 Conclusion -- References -- Chapter 6 Cognitive Radio (CR) Based Non-Orthogonal Multiple Access (NOMA) Network -- 6.1 Introduction -- 6.2 Fundamentals of CR -- 6.2.1 Spectrum Hole Approach -- 6.2.2 Physical Layout of CR -- 6.2.3 Characteristics of CR -- 6.2.3.1 Cognitive Capability -- 6.2.3.2 Reconfigurability -- 6.2.4 CR Paradigms -- 6.2.5 Multiple Access Scheme -- 6.3 Spectrum Management System -- 6.3.1 Spectrum Sensing -- 6.3.2 Spectrum Decision -- 6.3.3 Spectrum Sharing -- 6.3.4 Spectrum Mobility -- 6.4 Noma Networks -- 6.4.1 NOMA Classification -- 6.4.1.1 PD-NOMA -- 6.4.1.2 CD-NOMA -- 6.4.2 OMA vs. NOMA -- 6.4.3 Downlink NOMA -- 6.4.4 Uplink NOMA -- 6.4.5 CR-Based NOMA Network -- 6.5 Enabling Technologies -- 6.5.1 Millimeter Wave (mmWave) -- 6.5.2 Intelligent Reflecting Surfaces (IRS) -- 6.5.3 Simultaneous Wireless Information and Power Transfer (SWIPT).
6.5.4 Cooperative CR-Based NOMA Systems -- 6.5.5 Satellite Communication (SatCom) CR-Based NOMA Systems -- 6.6 Conclusion -- References -- Chapter 7 Artificial Intelligence and Machine Learning-Based Network Power Optimization Schemes -- 7.1 Introduction -- 7.2 Network -- 7.2.1 Working of Network -- 7.2.1.1 Client-Server Architecture -- 7.2.1.2 Network Protocols -- 7.2.1.3 Network Addresses -- 7.2.2 Network Methods -- 7.2.2.1 Wireless vs. Wired -- 7.2.2.2 Network Range -- 7.3 Decentralized Connection -- 7.4 Communication Network -- 7.4.1 Types of Communication Networks -- 7.4.2 Components of Communication Networks -- 7.4.3 Communication Protocols -- 7.4.4 Communication Medium -- 7.5 Internet of Things (IoT) -- 7.6 5G and Future Technologies -- 7.7 Network Power and Unstable Power Supply of Computer Networks -- 7.8 Adaption of Optimization Schemes to Enhance Network Power -- 7.9 Related Work -- 7.10 Traditional Evaluation AI and ML-Based Network Energy Optimization Techniques -- 7.11 AI- and ML-Based Systems for Network Energy Optimization Techniques -- 7.11.1 Problem Definition and Objectives -- 7.12 Conclusion -- References -- Chapter 8 Integration of PV Solar Rooftop Technology for Enhanced Performance and Sustainability of Electric Vehicles: A Techno-Analytical Approach -- 8.1 Introduction -- 8.1.1 Electric Vehicle -- 8.2 Literature Review -- 8.2.1 Numerous Challenges Faced by Electric Vehicles -- 8.3 Methods and Methodology -- 8.3.1 Structure of an Electric Vehicle Driven by Induction Motor -- 8.3.1.1 Solar Panel -- 8.3.1.2 Battery System -- 8.3.1.3 Motor Controller -- 8.3.1.4 Induction Motor -- 8.3.1.5 Power Electronics -- 8.3.1.6 Charging System -- 8.3.1.7 Energy Management System -- 8.3.1.8 Regenerative Braking System -- 8.3.1.9 Vehicle Control Unit -- 8.3.1.10 Mechanical Design -- 8.3.2 Contribution -- 8.4 Result and Discussion.
8.4.1 Modeling and Simulation of Induction Motor Used in Electric Vehicles -- 8.4.1.1 Dynamic Equations -- 8.4.1.2 Electric Dynamics -- 8.4.1.3 Magnetic Dynamic -- 8.4.1.4 Mechanical Dynamics -- 8.4.1.5 Equation of Motion -- 8.4.1.6 Electromagnetic Torque Equation -- 8.4.1.7 Synchronous Speed -- 8.4.1.8 Rotor Speed -- 8.4.1.9 Torque-Speed Characteristics -- 8.4.1.10 Load Torque -- 8.4.2 Outcomes and a Comparative Analysis of Our Proposed Photovoltaic (PV)-Based Electric Vehicle (EV) System -- 8.4.2.1 Simulation of an Induction Motor with Inverter -- 8.5 Conclusion -- References -- Chapter 9 The Viability of Advanced Technology for Future Generations -- 9.1 Introduction -- 9.2 Communication Systems -- 9.2.1 5G -- 9.2.2 6G -- 9.2.3 Quantum Communications -- 9.2.4 Satellite Communication -- 9.2.5 Holography -- 9.2.6 Brain Computer Interface (BCI) -- 9.2.7 Artificial Intelligence (AI) -- 9.2.8 Internet of Things (IOT) -- 9.3 Conclusion -- References -- Chapter 10 Power Optimization and Scheduling for Multi-Layer, Multi-Dimensional 6G Communication Networks -- 10.1 Introduction -- 10.1.1 Background -- 10.1.2 Motivation -- 10.2 Literature Review -- 10.2.1 Evolution of Communication Networks -- 10.2.2 Key Features and Requirements of 6G -- 10.2.3 Previous Approaches to Power Optimization and Scheduling -- 10.3 Multi-Layer, Multi-Dimensional 6G Communication Networks -- 10.3.1 Architecture Overview -- 10.3.2 Integration of Multiple Layers -- 10.3.3 Consideration of Various Dimensions -- 10.4 Power Optimization in MLMD 6G Networks -- 10.4.1 Challenges in Power Consumption -- 10.4.2 Machine Learning Approaches -- 10.4.3 Adaptive Power Management -- 10.5 Scheduling Strategies for MLMD 6G Networks -- 10.5.1 Dimensions Considered in Scheduling -- 10.5.2 Resource Allocation Algorithms -- 10.5.3 Interference Mitigation Techniques -- 10.6 Proposed Framework.
10.6.1 Integration of Power Optimization and Scheduling.
Record Nr. UNINA-9911020462803321
Chopra Shakti Raj  
Newark : , : John Wiley & Sons, Incorporated, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
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