Cover
Preface
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
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
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
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
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
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
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
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
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
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
Index
End User License Agreement

List of Tables

Chapter 4
1. Table 4.1 Applications of cognitive computing in wireless networks.
Chapter 5
1. Table 5.1 Learning techniques of cognitive radio networks.
2. Table 5.2 Summary of game theory models for spectrum allocation.
Chapter 7
1. Table 7.1 Advanced classes of radio properties.
2. Table 7.2 Selected genes and their ranges.
3. Table 7.3 Frequency gene representation.
4. Table 7.4 Power gene representation.
5. Table 7.5 BER gene representation.
6. Table 7.6 Modulation gene representation.
7. Table 7.7 Chromosome structure.
8. Table 7.8 Assumed conditions.
9. Table 7.9 Comparison of cooperative wideband spectrum-sensing algorithms.
Chapter 8
1. Table 8.1 Comparison table for BER and PAPR of original and proposed methods.
Chapter 9
1. Table 9.1 Illustrations of the total energy of the network, FND, HND, LND, th...

List of Illustrations

Chapter 1
1. Figure 1.1 Linear classifier.
2. Figure 1.2 Linear classifier for SVM.
3. Figure 1.3 Linear classifier with hyperplane in SVM.
4. Figure 1.4 Linear classifier for SVM with wider hyperplane.
5. Figure 1.5 Non-linear SVMs: Feature spaces.
6. Figure 1.6 Non linear SVM where features are converted into higher dimension...
7. Figure 1.7 Hyperplanes and support vectors.
8. Figure 1.8 Continuous ML model and control framework
9. Figure 1.9 Machine learning architecture
10. Figure 1.10 ML architecture: data acquisition
Chapter 2
1. Figure 2.1 Flowchart of cognitive architecture
2. Figure 2.2 Structure of a model based on a cognitive architecture (Byrne, M....
3. Figure 2.3 Human-centered cognitive cycle.
4. Figure 2.4 Perceptual and rational method to recognize a square. (a) Rationa...
5. Figure 2.5 The system architecture of cognitive computing
6. Figure 2.6 IBM Watson utilized in healthcare
7. Figure 2.7 High autonomy cognitive architecture
8. Figure 2.8 Cognitive computing applications
Chapter 3
1. Figure 3.1 The machine and deep learning pictorial representation [38].
2. Figure 3.2 Deep reinforcement learning [48].
3. Figure 3.3 Taxonomy of the applications of deep reinforcement learning for c...
Chapter 4
1. Figure 4.1 Evolution of cognitive computing.
2. Figure 4.2 Characteristics of cognitive computing.
3. Figure 4.3 Basic architecture of cognitive computing framework.
4. Figure 4.4 Integration of various technologies for smart communication.
5. Figure 4.5 Cognitive radio cycle.
6. Figure 4.6 Framework of a multi agent based system.
7. Figure 4.7 Wireless communication architecture based on cognitive computing ...
8. Figure 4.8 Architecture of protocol design using reinforcement learning.
9. Figure 4.9 Basic communication building blocks.
10. Figure 4.10 Basic deep learning architecture for channel decoding.
11. Figure 4.11 LSTM based detectors.
12. Figure 4.12 Smart healthcare application.
Chapter 5
1. Figure 5.1 Dynamic spectrum allocation between PUs and SUs.
2. Figure 5.2 Spectrum sensing cycle.
3. Figure 5.3 Classification of spectrum sensing techniques.
4. Figure 5.4 Block diagram of energy detection model.
5. Figure 5.5 A simplified block diagram of matched filter detection technique ...
6. Figure 5.6 A typical block schematic of cyclo-stationary detection technique...
7. Figure 5.7 A typical block schematic of Euclidean distance-based detection t...
8. Figure 5.8 A basic neural network model [79].
9. Figure 5.9 Scheme of ANN.
Chapter 6
1. Figure 6.1 Fundamental architecture of IoT.
2. Figure 6.2 IoT topologies.
3. Figure 6.3 IoT networks.
4. Figure 6.4 IoT networks.
Chapter 7
1. Figure 7.1 xG network/DSANs/CRAHN architecture.
2. Figure 7.2 CR network on a licensed band.
3. Figure 7.3 CR network on an unlicensed band.
4. Figure 7.4 CR physical architecture: (a) CR transceiver and (b) RF front-end...
5. Figure 7.5 Communication functionalities of a CR network.
6. Figure 7.6 Spectrum hole concept.
7. Figure 7.7 Spectrum sharing access techniques: (a) underlay and (b) overlay....
8. Figure 7.8 CR network accessing an authorized band.
9. Figure 7.9 CR network accessing an unauthorized band.
10. Figure 7.10 Cognitive radio network.
11. Figure 7.11 Interferers are detected in a CR network.
12. Figure 7.12 A CR network self-adjusts its characteristics.
13. Figure 7.13 A CR network self-adjusts its characteristics.
14. Figure 7.14 (a) Cooperative centralized technique. (b) Cooperative centraliz...
Chapter 8
1. Figure 8.1 Cognitive radio cycle.
2. Figure 8.2 Block diagram of TX NN and RX NN using ACE schemes.
3. Figure 8.3 Input signal.
4. Figure 8.4 PAPR reduction over different sampling rates.
5. Figure 8.5 BER comparison of OICF and ANN methods.
6. Figure 8.6 Spectral efficiency comparison for S-PTS, OICF, and ANN methods....
Chapter 9
1. Figure 9.1 Alive nodes vs number of rounds.
2. Figure 9.2 Dead nodes vs number of rounds.
3. Figure 9.3 First node dead (FND), half node dead (HND), and last node dead (...
4. Figure 9.4 Energy consumption vs number of rounds.
5. Figure 9.5 Packets sent to BS vs number of rounds.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-64036-3

Cover image: Pixabay.Com
Cover design by Russell Richardson

Preface

The rapid advancements that have recently taken place in communication and network technology have resulted in numerous information services and applications being developed globally. These technological advances continue to have a high impact on society by affecting the way we lead our lives, and although they have undoubtedly improved quality of service and user experience, there is still much to be done. With that in mind, our main objective in writing this book is to address the many challenges associated with this technology and to provide a platform for machine learning-en-abled mobile communications and wireless networks.

Since it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication, in addition to providing mathematical and conceptual background on the latest technology, various areas still needing improvement are discussed in this book, including seamless wide-area coverage, high-capacity hotspots, low-power massive connections, and low-latency and high-reliability wireless networks. In addition to mobile communications and wireless networks, machine-learning techniques are also discussed, including machine-learning architecture and framework, deep reinforcement learning for wireless networks, machine learning–based security and privacy protection for communications/networks, spectrum-aware mobile computing, wireless technology in the Internet of Things (IoT), infrastructure in mobile opportunistic networks, and spectrum allocation in cognitive radio.

Machine learning and cognitive computing have converged to provide some groundbreaking solutions for smart machines. With these two technologies coming together, machines can acquire the ability to reason similar to the human brain. Since the research area of machine learning and cognitive computing covers many fields that can be used effectively for topology management, such as psychology, biology, signal processing, physics, information theory, mathematics, and statistics, the utilization of machine-learning techniques like data analytics and cognitive power will lead to a better performance of communication and wireless systems.

Based on the dominance of the emergent field of machine learning and cognitive computing as applied to mobile communications and wireless networks and the aforementioned facts, this book is an essential guide for all academicians, researchers, and those working in industry in the related field. Because it is an amalgamation of theory, mathematics, and examples of the discussed technologies, this book will also be relevant to all levels of students—from undergraduate and postgraduate to research students— studying computer science or electronics.

Krishna Kant Singh
Akansha Singh
Korhan Cengiz
Dac-Nhuong Le
May 2020