Intelligent Communication Networks : Research and Applications

Intelligent Communication Networks Research and Applications

The text presents the basic understanding of the machine learning algorithms used for communication networks in a single volume. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in diverse engineering domains including electrical, electro...

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Detalles Bibliográficos
Otros Autores: Mahapatra, Rajarshi, editor (editor), Bhattacharyya, Siddhartha, editor, Nag, Avishek, editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press [2024]
Edición:First edition
Materias:
Telecommunication systems > Technological innovations.
Artificial intelligence > Engineering applications.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009869139906719
Tabla de Contenidos:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Dedication
  • Table of Contents
  • Preface
  • Editors
  • Contributors
  • Chapter 1: Various Deep Learning-based Resource Allocation Techniques in Wireless Communication System
  • 1.1 The Next-Generation Wireless Communication Systems
  • 1.2 Machine Learning Algorithms
  • 1.2.1 Deep Neural Networks
  • 1.2.2 Deep Reinforcement Learning
  • 1.2.3 Centralized Learning
  • 1.2.4 Distributed Learning
  • 1.2.5 Federated Learning
  • 1.2.6 Deep Learning in Resource Allocation
  • 1.3 Heterogeneous Network
  • 1.4 Reinforcement Learning
  • 1.5 Federated Learning
  • 1.6 Federated Deep Reinforcement Learning in Resource Allocation
  • 1.6.1 Overview of Federated Deep Reinforcement Learning (FDRL) in Resource Allocation
  • 1.6.2 Applying Federated Deep Reinforcement Learning in Resource Allocation
  • 1.6.2.1 Challenges and Advantages
  • References
  • Chapter 2: Federated Deep Reinforcement Learning-based Resource Allocation in Heterogeneous Networks
  • 2.1 Introduction
  • 2.1.1 Supervised Learning-based Resource Allocation
  • 2.1.2 Unsupervised Learning-based Resource Allocation
  • 2.1.3 Learning Assisted Optimization for Resource Allocation
  • 2.1.4 Deep Reinforcement Learning for Resource Allocation
  • 2.1.4.1 Deep Q-Networks
  • 2.1.4.2 Deep Deterministic Policy Gradient
  • 2.1.5 Federated Learning-based Resource Allocation
  • 2.2 Reinforcement Learning in Resource Allocation
  • 2.3 Aim of the Chapter
  • 2.4 System Model
  • 2.5 Proposed Federated-DRL Algorithm
  • 2.6 Simulation Results
  • 2.6.1 Spectral Efficiency Performance
  • 2.6.2 Timing and Complexity Analysis
  • 2.7 Summary
  • References
  • Chapter 3: A Comprehensive Overview of Internet of Nano-Things (IoNT) in the Next-Generation Heterogeneous Networks: Deployment Aspects, Applications, and Challenges
  • 3.1 Introduction.
  • 3.2 Comparison to Other Studies
  • 3.2.1 5G and IoNT
  • 3.2.2 IoNT Characteristics and Architecture
  • 3.2.2.1 IoNT Characteristics
  • 3.2.2.2 IoNT Architecture
  • 3.2.3 IoNT-Enabling Technologies
  • 3.2.4 IoNT Applications
  • 3.2.5 IoNT Network Architecture and Standards
  • 3.2.5.1 Physical Layer
  • 3.2.5.2 Link Layer
  • 3.2.5.3 Network Layer
  • 3.2.5.4 Upper Layers
  • 3.2.5.5 Standards
  • 3.2.6 Big Data Analytics, Cloud Computing, and Fog Computing in Support of the IoNT
  • 3.2.6.1 Big Data Analytics in Support of the IoNT
  • 3.2.6.2 Cloud Computing in Support of the IoNT
  • 3.2.6.3 Fog Computing in Support of the IoNT
  • 3.2.7 Deployment Aspects of the IoNT
  • 3.2.7.1 Random vs. Deterministic
  • 3.2.7.2 Static vs. Dynamic
  • 3.2.8 Open Research Issues and Future Challenges
  • 3.2.8.1 Architecture and Protocols
  • 3.2.8.2 Reliability
  • 3.2.8.3 Throughput
  • 3.2.8.4 Lifetime
  • 3.2.8.5 Data Collection and Routing Technology
  • 3.2.8.6 Security and Privacy
  • 3.2.8.7 Service Discovery
  • 3.2.8.8 Context Awareness
  • 3.3 Conclusion
  • References
  • Chapter 4: Emerging World of the Metaverse: An Indian Perspective
  • 4.1 Introduction
  • 4.2 Internet of Things and Virtual Reality
  • 4.3 Emergence of the Metaverse
  • 4.4 Network and Communication in the Metaverse
  • 4.4.1 Building Smart Cities with the Metaverse
  • 4.4.2 Industry 4.0 and Digital Twin
  • 4.5 Socialization in the Metaverse
  • 4.6 The Metaverse in India
  • 4.7 Technological Limitations
  • 4.8 Data Safety and Security
  • 4.9 Dilemmas and Conundrum of the Metaverse
  • 4.10 Future of the Metaverse
  • 4.10.1 Metaverse for Business Ideas
  • 4.10.2 Customer User Engagement
  • 4.10.3 Legal Framework in the Metaverse
  • 4.11 Conclusion
  • References
  • Chapter 5: Intelligent Optical Networks: Challenges, Opportunities, and Applications
  • 5.1 Introduction.
  • 5.2 Overview of Intelligent Optical Networks
  • 5.2.1 Evolution of Optical Network Transmission Technology
  • 5.2.2 Optical Network Functionality
  • 5.2.3 Categories of Optical Networks
  • 5.2.4 Classification of Machine Learning
  • 5.2.4.1 Supervised Learning
  • 5.2.4.2 Unsupervised Learning
  • 5.2.4.3 Reinforcement Learning
  • 5.2.5 Techniques for Generation of Data and Modeling Images
  • 5.2.6 Metrics-Classification Metrics, Regression Metrics, and Rand and Jaccard Indices
  • 5.3 Challenges and Opportunities in Intelligent Optical Networks
  • 5.3.1 Challenges in Intelligent Optical Networks
  • 5.3.2 Opportunities in Intelligent Optical Networks
  • 5.4 Applications in Intelligent Optical Networks
  • 5.4.1 Artificial Intelligence in Optical Networks
  • 5.4.2 Machine Learning in Optical Networks
  • 5.4.2.1 ML Approaches to Physical Layer Applications
  • 5.4.2.2 ML Approaches to Network Layer Applications
  • 5.4.3 Big Data Analytics in Optical Networks
  • 5.5 Various Simulation Tools for Intelligent Optical Networks
  • 5.5.1 Simulation Study
  • 5.5.2 Result and Inferences
  • 5.6 Conclusion
  • Acknowledgement
  • References
  • Chapter 6: Machine Learning for Non-Orthogonal Multiple Access
  • 6.1 Introduction: Background and Driving Forces
  • 6.2 PD NOMA
  • 6.3 CR NOMA
  • 6.4 Multi-Carrier NOMA
  • 6.5 Cooperative NOMA
  • 6.5.1 Employing Dedicated Relays
  • 6.6 Millimeter Wave NOMA
  • 6.7 Detection in NOMA: From SIC to Deep Learning
  • 6.8 Practical Implementation of NOMA
  • 6.8.1 Modulation and Coding for NOMA
  • 6.8.2 Imperfect CSI
  • 6.9 NOMA with Machine Learning
  • 6.9.1 Different Aspect of ML in NOMA Networks
  • 6.10 Future Challenges
  • 6.10.1 NOMA with Heterogeneous Networks
  • 6.10.2 NOMA with Simultaneous Wireless Information and Power Transfer (SWIPT)
  • 6.10.3 NOMA with Visible Light Communication (VLC)
  • 6.11 Conclusions
  • References.
  • Chapter 7: Compensating Inbound Signal Strength for Radio-Controlled Mobile Robots Using ANFIS
  • 7.1 Introduction
  • 7.2 Using Fuzzy Logic to Create Telecommunication Systems
  • 7.3 The ANFIS Method
  • 7.4 Training a Neuro-Fuzzy System Based on the Area Difference Method
  • 7.5 Aggregation of Output Values
  • 7.6 Conclusions
  • Acknowledgment
  • Author Contributions
  • References
  • Chapter 8: Optimizing Wireless Sensor Networks Using Machine Learning
  • 8.1 Introduction
  • 8.2 Sensor Network
  • 8.2.1 Types of WSN
  • 8.2.2 Applications of WSN
  • 8.2.3 Challenges in WSN and Design Objectives
  • 8.3 Machine Learning (ML)
  • 8.3.1 Supervised Machine Learning
  • 8.3.2 Unsupervised Learning
  • 8.3.2.1 Clustering
  • 8.3.2.2 Dimension Reduction
  • 8.3.3 Reinforcement Learning
  • 8.3.4 Deep Learning
  • 8.4 ML for Optimizing WSNs
  • 8.4.1 Security
  • 8.4.1.1 Anomaly Detection
  • 8.4.1.2 Intrusion Detection
  • 8.4.2 Routing
  • 8.4.3 Data Aggregation
  • 8.4.4 Coverage and Connectivity
  • 8.4.5 Localization
  • 8.4.6 Quality of Service (QoS)
  • 8.4.7 Energy Harvesting
  • 8.4.8 Congestion Control
  • 8.4.9 Challenges of ML for WSN
  • 8.5 ML in WSN Applications
  • 8.6 Conclusions
  • References
  • Chapter 9: Machine Learning-Assisted Interference Management in the 6G UAV Networks with Soft Frequency Reuse
  • 9.1 Introduction
  • 9.2 Related Works
  • 9.3 UAV-Assisted Wireless Networks
  • 9.4 Soft Frequency Reuse in UAV-Assisted Networks
  • 9.5 Proposed Machine Learning Model
  • 9.6 Simulation Results and Analysis
  • 9.7 Conclusions
  • References
  • Chapter 10: Computational Intelligence in Communication Networks: Classification, Clustering, Reinforcement Learning, Deep Learning
  • 10.1 Introduction
  • 10.1.1 Neural Networks
  • 10.1.2 Fuzzy Systems
  • 10.1.3 Evolutionary Computation
  • 10.2 Differences Between Computational Intelligence and Artificial Intelligence.
  • 10.3 Details of Computational Intelligence
  • 10.3.1 Branches of Computational Intelligence
  • 10.3.2 Principles of CI and Application
  • 10.3.2.1 Fuzzy Logic
  • 10.3.2.2 Neural Networks
  • 10.3.2.3 Evolutionary Computation
  • 10.3.2.4 Natural Language Processing
  • 10.3.2.5 Probabilistic Methods
  • 10.3.3 Implementation of CI
  • 10.3.3.1 Using a Mobile Phone as an Example
  • 10.4 Computational Intelligence in Communication Network
  • 10.5 Conclusion
  • References
  • Index.

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