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