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Book Description

Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.

Table of Contents

  1. Cover
  2. Preface
  3. 1 Machine Learning Architecture and Framework
    1. 1.1 Introduction
    2. 1.2 Machine Learning Algorithms
    3. 1.3 Business Use Cases
    4. 1.4 ML Architecture Data Acquisition
    5. 1.5 Latest Application of Machine Learning
    6. 1.6 Future of Machine Learning
    7. 1.7 Conclusion
    8. References
  4. 2 Cognitive Computing: Architecture, Technologies and Intelligent Applications
    1. 2.1 Introduction
    2. 2.2 The Components of a Cognitive Computing System
    3. 2.3 Subjective Computing Versus Computerized Reasoning
    4. 2.4 Cognitive Architectures
    5. 2.5 Subjective Architectures and HCI
    6. 2.6 Cognitive Design and Evaluation
    7. 2.7 Cognitive Technology Mines Wealth in Masses of Information
    8. 2.8 Cognitive Computing: Overview
    9. 2.9 The Future of Cognitive Computing
    10. References
  5. 3 Deep Reinforcement Learning for Wireless Network
    1. 3.1 Introduction
    2. 3.2 Related Work
    3. 3.3 Machine Learning to Deep Learning
    4. 3.4 Applications of Machine Learning Models in Wireless Communication
    5. 3.5 Conclusion
    6. References
  6. 4 Cognitive Computing for Smart Communication
    1. 4.1 Introduction
    2. 4.2 Cognitive Computing Evolution
    3. 4.3 Characteristics of Cognitive Computing
    4. 4.4 Basic Architecture
    5. 4.5 Resource Management Based on Cognitive Radios
    6. 4.6 Designing 5G Smart Communication with Cognitive Computing and AI
    7. 4.7 Advanced Wireless Signal Processing Based on Deep Learning
    8. 4.8 Applications of Cognition-Based Wireless Communication
    9. 4.9 Conclusion
    10. References
  7. 5 Spectrum Sensing and Allocation Schemes for Cognitive Radio
    1. 5.1 Foundation and Principle of Cognitive Radio
    2. 5.2 Spectrum Sensing for Cognitive Radio Networks
    3. 5.3 Classification of Spectrum Sensing Techniques
    4. 5.4 Energy Detection
    5. 5.5 Matched Filter Detection
    6. 5.6 Cyclo-Stationary Detection
    7. 5.7 Euclidean Distance-Based Detection
    8. 5.8 Spectrum Allocation for Cognitive Radio Networks
    9. 5.9 Challenges in Spectrum Allocation
    10. 5.10 Future Scope in Spectrum Allocation
    11. References
  8. 6 Significance of Wireless Technology in Internet of Things (IoT)
    1. 6.1 Introduction
    2. 6.2 Overview of the Hardware Components of IoT
    3. 6.3 Wireless Technology in IoT
    4. 6.4 Conclusion
    5. References
  9. 7 Architectures and Protocols for Next-Generation Cognitive Networking
    1. 7.1 Introduction
    2. 7.2 Cognitive Radio Network Technologies and Applications
    3. 7.3 Cognitive Computing: Architecture, Technologies, and Intelligent Applications
    4. 7.4 Functionalities of CR in NeXt Generation (xG) Networks
    5. 7.5 Spectrum Sensing
    6. 7.6 Cognitive Computing for Smart Communications
    7. 7.7 Spectrum Allocation in Cognitive Radio
    8. 7.8 Cooperative and Cognitive Network
    9. References
  10. 8 Analysis of Peak-to-Average Power Ratio in OFDM Systems Using Cognitive Radio Technology
    1. 8.1 Introduction
    2. 8.2 OFDM Systems
    3. 8.3 Peak-to-Average Power Ratio
    4. 8.4 Cognitive Radio (CR)
    5. 8.5 Related Works
    6. 8.6 Neural Network System Model
    7. 8.7 Complexity Examination
    8. 8.8 PAPR and BER Examination
    9. 8.9 Performance Evaluation
    10. 8.10 Results and Discussions
    11. 8.11 Conclusion
    12. References
  11. 9 A Threshold-Based Optimization Energy-Efficient Routing Technique in Heterogeneous Wireless Sensor Networks
    1. 9.1 Introduction
    2. 9.2 Literature Review
    3. 9.3 System Model
    4. 9.5 Simulation Results and Discussions
    5. 9.6 Conclusion
    6. References
  12. 10 Efficacy of Big Data Application in Smart Cities
    1. 10.1 Introduction
    2. 10.2 Types of Data in Big Data
    3. 10.3 Big Data Technologies
    4. 10.4 Data Source for Big Data
    5. 10.5 Components of a Smart City
    6. 10.6 Challenge and Solution of Big Data for Smart City
    7. 10.7 Conclusion
    8. References
  13. Index
  14. End User License Agreement
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