Developments in Cognitive Radio Networks: Future Directions for Beyond 5G

✍ Scribed by Bodhaswar TJ Maharaj, Babatunde Seun Awoyemi

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 256
• Edition: 1
• Category: Library

✦ Synopsis

This book provides holistic yet concise information on what modern cognitive radio networks are, how they work, and the possible future directions for them. The authors first present the most generic models of modern cognitive radio networks, taking into consideration their different architectural designs and classifications. While the spectrum resource is shown to be the most important resource for the cognitive radio networks, the book exposes the importance of the other resources that are needed to help drive the technology. The book then discusses in-depth the key tools (such as optimization and queuing theory) and techniques (such as cooperative diversity and relaying) that are being employed to formulate resource problems, investigate solutions, and interpret such solutions for useful and practical modern cognitive radio networks realization. Further, the book studies the impact of modern cognitive radio networks on other emerging technologies -- such as 5G, Internet of Things, and advanced wireless sensor networks -- and discusses the role that cognitive radio networks play in the evolution of smart cities and in the realization of a highly interconnected world. In discussing the future of the cognitive radio networks, the book emphasizes the need to advance new or improved tools, techniques, and solutions to address lingering problems in the aspects of resource realization and utilization, network complexity, network security, etc., which can potentially limit the cognitive radio networks in their stride to becoming one of the most promising technologies for the immediate and near future.

✦ Table of Contents

Foreword
Preface
Acknowledgements
Contents
Acronyms
Symbols
Part I Fundamentals on Cognitive Radio Networks
1 Introduction to Cognitive Radio Networks
1.1 A Growing Demand for Wireless Communication
1.2 History of Cognitive Radio Networks
1.3 Application of Dynamic Spectrum Access in Cognitive Radio Networks
1.4 Cognitive Radio Network Beyond Spectrum
1.5 Possible Limitations with Cognitive Radio Network Applications
1.5.1 Resource Limitations
1.5.2 Network Complexity
1.5.3 Problem of Interference
1.5.4 Limitations of Wireless Communication
1.6 Summary of the Chapter
References
2 Perspectives on Cognitive Radio Networks
2.1 Architectural Descriptions of Cognitive Radio Networks
2.1.1 Centralised, Distributed or Mesh
2.1.2 Overlay, Underlay or Hybrid
2.1.3 Cooperative or Non-cooperative
2.2 Cognitive Radio Networks as Heterogeneous Systems
2.2.1 Heterogeneous Network
2.2.2 Heterogeneous Users or User Demands
2.2.2.1 Quality of Service Requirements
2.2.2.2 Service Type or Traffic Demands
2.2.2.3 Service Availability
2.2.2.4 User Sensitivity
2.2.3 Heterogeneous Channels
2.3 Technologies to Drive Cognitive Radio Network
2.3.1 Cooperative Diversity and Relaying
2.3.2 Massive MIMO and Beamforming
2.3.3 Cloud Computing
2.3.4 Orthogonal and Non-orthogonal Multiple Access
2.4 Summary of the Chapter
References
3 Spectrum Resource for Cognitive Radio Networks
3.1 The Place of Spectrum in the Overall Cognitive Radio Network Scheme
3.2 Historical Context on the Spectrum in Cognitive Radio Networks
3.3 Spectrum Sensing in Cognitive Radio Networks
3.3.1 Energy Detection Techniques
3.3.2 Matched Filter Detection Techniques
3.3.3 Cyclo-stationary Feature Detection Techniques
3.3.4 Waveform-Based Sensing Techniques
3.3.5 Radio Identification Sensing Techniques
3.3.6 Techniques that Employ Multiple Antennas
3.4 Problems Associated with Spectrum Sensing in Cognitive Radio Networks
3.4.1 The Hidden Node Problem
3.4.2 The Problem of Shadowing
3.4.3 The Problem of Multipath Fading
3.4.4 The Problem of Receiver Uncertainty
3.5 Determining Sensing Accuracy
3.6 Cooperative Spectrum Sensing in Cognitive Radio Networks
3.6.1 Benefits of Cooperative Sensing
3.6.2 The Cost of Cooperative Sensing
3.6.3 Techniques for Cooperative Sensing
3.7 Spectrum Prediction for Cognitive Radio Network Applications
3.7.1 Artificial Intelligence Models
3.7.2 Linear Models
3.7.3 Statistical Models
3.8 Cooperative Spectrum Prediction for Cognitive Radio Network Applications
3.9 Practical Examples of Recent Campaign Efforts on Spectrum Availability
3.9.1 Spectrum Measurement Campaign on the UHF and GSM Bands
3.9.2 Spectrum Measurement Campaign on the TV Broadcast Bands
3.10 Summary of the Chapter
References
Part II Resources to Drive Cognitive Radio Networks
4 Resource Optimisation Problems in Cognitive Radio Networks
4.1 Complementary Resources for Cognitive Radio Network Applications
4.2 Resource Allocation in Cognitive Radio Networks
4.3 Resource Allocation Problems in Cognitive Radio Networks
4.4 Resource Allocation in Cognitive Radio Networks as Optimisation Problems
4.5 A General Representation of the Resource Allocation Problems in Cognitive Radio Networks
4.6 Unique Characteristics of Resource Allocation Optimisation Problems in Cognitive Radio Networks
4.7 Useful Observations on the Resource Allocation Optimisation Problems in Cognitive Radio Networks
4.8 Summary of the Chapter
References
5 Tools for Resource Optimisation in Cognitive Radio Networks
5.1 Solving Resource Allocation Problems in Cognitive Radio Networks
5.2 The Tool of Classical Optimisation
5.3 The Tool of Studying the Structure of the Resource Allocation Problems
5.3.1 The Method of Separation or Decomposition
5.3.2 The Method of Relaxation
5.3.3 The Method of Linearisation
5.3.4 The Method of Reformulation
5.3.5 The Method of Approximation
5.4 The Tool of Heuristics
5.4.1 Greedy Algorithms
5.4.2 Water-Filling Schemes
5.4.3 Recursive-Based and Iterative-Based Heuristics
5.4.4 Pre-assignment and Reassignment Algorithms
5.5 The Tool of Meta-heuristics
5.5.1 Genetic Algorithms
5.5.2 Simulated Annealing
5.5.3 Tabu Searches
5.5.4 Evolutionary Algorithms
5.6 The Tool of Multi-objective Optimisation and Game Theory
5.7 The Tool of Soft Computer-Based Optimisation
5.8 Summary of the Chapter
References
6 Modelling and Analyses of Resource Allocation Optimisation in Cognitive Radio Networks
6.1 Need for Resource Allocation Models in Cognitive Radio Networks
6.2 System Modelling for Resource Allocation in Heterogeneous Cognitive Radio Networks
6.2.1 Modelling Underlay Cognitive Radio Networks
6.2.2 Modelling Overlay Cognitive Radio Networks
6.2.3 Modelling Hybrid Cognitive Radio Networks
6.3 Representing User Heterogeneity in the Resource Allocation Modelling for Cognitive Radio Networks
6.4 General Formulation of Resource Allocation Problems in Cognitive Radio Networks
6.5 General Problem Formulation While Employing Other Heterogeneous User Classifications
6.6 Relating Other Resource Allocation Problem Formulations to the General Formulation
6.6.1 Other Underlay Formulations in Relation to the General Formulation
6.6.2 Other Overlay Formulations in Relation to the General Formulation
6.6.3 Other Hybrid Formulations in Relation to the General Formulation
6.7 Exploring Practical Solutions for the Resource Allocation Problems in Cognitive Radio Networks
6.7.1 Studying the Structure of the Problems
6.7.2 Problem Reformulation
6.7.3 Classical Optimisation Solutions
6.7.4 Other Possible Solution Methods
6.8 Important Results from the Resource Allocation Modelling and Solution for Heterogeneous Cognitive Radio Networks
6.8.1 The Effects of Interference on the Bit Allocation
6.8.2 Average and Total Data Rates
6.8.3 Effects of Weight
6.9 Summary of the Chapter
References
Part III New Directions in the Development of Cognitive Radio Networks
7 Queuing Systems in Resource Allocation Optimisation for Cognitive Radio Networks
7.1 Queuing-Related Problems in Heterogeneous Cognitive Radio Network
7.2 Description of Queuing Theory for Resource Allocation in Cognitive Radio Network
7.3 Queuing-Based Resource Allocation Solutions for Cognitive Radio Network
7.4 Queuing Model for Multi-Modal Switching Service Levels
7.4.1 Different Modes as Different Service Levels
7.4.2 Description of the System Model for Multi-Modal Switching Service Levels
7.4.3 Analysis and Performance Results of the Multi-Modal Switching Model
7.5 Queuing Model for Increased Spectrum Utilisation
7.5.1 System Model and Analysis of the Queuing Model for Increased Spectrum Utilisation
7.5.2 Performance Results of the Queuing Model for Increased Spectrum Utilisation
7.6 Queuing Model for Heterogeneous Users with Different Delay Profiles
7.6.1 System Model of the Heterogeneous Buffered Cognitive Radio Network
7.6.2 Model Analysis of the Heterogeneous Buffered Cognitive Radio Network
7.6.3 Performance Results for the Heterogeneous Buffered Cognitive Radio Network
7.7 Performance Evaluation of Queuing-Related Resource Allocation Solutions in Cognitive Radio Networks
7.8 Performance Framework for Queuing-Based Resource Allocation in Cognitive Radio Network
7.8.1 System Model and Analysis of the Performance Framework
7.8.2 Benefit of a Performance Framework
7.9 Performance Implications of Queuing-Based Resource Allocation in Cognitive Radio Networks
7.9.1 Important Performance Measures
7.9.2 Implications of Proper Performance Evaluation
7.10 Summary of the Chapter
References
8 Cooperative Diversity for Resource Optimisation in Cognitive Radio Networks
8.1 The Problem of Interference in Cognitive Radio Networks
8.2 Attempts at Solving the Interference Problem in Cognitive Radio Networks
8.3 Cooperative Diversity Approach to Solving the Interference Problem in Cognitive Radio Networks
8.4 Recent Works on Cooperative Diversity for End-2-End Communication in Cognitive Radio Networks
8.5 A System Model for Cooperative-Based Resource Optimisation in Cognitive Radio Network
8.6 The Relay-Selection Process in Cooperative Diversity for Cognitive Radio Networks
8.7 Problem Formulation of Resource Allocation in Heterogeneous Cooperative Cognitive Radio Network
8.8 Optimal Solution for the Resource Allocation Problem in Heterogeneous Cooperative Cognitive Radio Networks
8.9 Heuristic Solution for the Resource Allocation Problem in Heterogeneous Cooperative Cognitive Radio Networks
8.9.1 Subchannel Allocation
8.9.2 Iterative Bit and Power Allocation
8.10 Useful Results from the Resource Allocation for Heterogeneous Cooperative Cognitive Radio Networks
8.11 Summary of the Chapter
References
9 Interference Management and Control in Cognitive Radio Networks Using Stochastic Geometry
9.1 Interference Management Through Stochastic Geometry
9.2 Advantages of Stochastic Geometry over the Conventional Hexagonal Grid Model
9.3 Users' Distributions Modelling in Cognitive Radio Networks
9.3.1 Poisson Point Process
9.3.2 Binomial Point Process
9.3.3 Hardcore Point Processes
9.3.3.1 Distance-Based Exclusion Regions
9.3.3.2 Threshold-Based Exclusion Regions
9.3.4 Poisson Cluster Process
9.4 Analysis of the Signal-to-Interference Plus Noise Ratio
9.4.1 Interference Modelling in the Primary Network
9.4.2 Interference Modelling in the Secondary Network
9.5 Summary of the Chapter
References
10 Deep Learning Opportunities for Resource Management in Cognitive Radio Networks
10.1 Introducing Machine and Deep Learning into Cognitive Radio Networks
10.2 Understanding Machine Learning and Deep Learning
10.3 Incorporating Deep Learning in Wireless Networks
10.4 Training a Deep Learning Model
10.5 Application of Deep Learning in Spectrum Management
10.6 Deep Reinforcement Learning
10.6.1 Training the Deep Reinforcement Learning Model
10.6.2 Application of Deep Reinforcement Learning in Spectrum Management
10.7 Hierarchical Deep Architectures for Cognitive Radio Networks Applications
10.7.1 General Model of a Hierarchical Deep Architecture
10.7.2 Application of Hierarchical Deep Reinforcement Learning in Cognitive Radio Networks and Edge Computing
10.7.3 Application of Hierarchical Deep Reinforcement Learning in Cognitive Radio Networks Energy Management
10.8 Summary of the Chapter
References
11 The Role of Cognitive Radio Networks in Fifth-Generation Communication and Beyond
11.1 Next-Generation Wireless Communication Technologies
11.2 The Role of Cognitive Radio Networks in Fifth-Generation Communication
11.3 The Role of Cognitive Radio Networks in Internet-of-Things Networking
11.4 The Role of Cognitive Radio Networks in Advanced Wireless Sensor Networks
11.5 The Role of Cognitive Radio Networks in Smart Cities
11.6 The Role of Cognitive Radio Networks in 6G, 4IR and Other Emerging Technologies
11.7 Essentials for Practicable Application of Cognitive Radio Networks in Next-Generation Communications
11.8 Summary of the Chapter
References
12 Future Opportunities for Cognitive Radio Networks
12.1 Problems Yet Unsolved in Cognitive Radio Networks
12.2 Problems Associated with Optimisation in Cognitive Radio Networks
12.3 Problems Associated with Queueing Theory in Cognitive Radio Networks
12.4 Problems Associated with the Use of Stochastic Geometry in Cognitive Radio Networks
12.5 Problems Associated with the Use of Machine and Deep Learning in Cognitive Radio Networks
12.6 Other General Problems Still Associated with the Cognitive Radio Networks
12.7 Recommendations and Research Directions for Further Developments in Cognitive Radio Networks
12.7.1 Recommendations for Further Improvement in Optimisation and Queueing Modelling
12.7.2 Recommendations for Improving Cooperation-Based Solutions
12.7.3 Recommendations for Improving Interference Management Through the Use of Stochastic Geometry
12.7.4 Recommendations for Improving Machine and Deep Learning Applications and Implementations
12.7.5 Other General Recommendations and Research Directions
12.8 Concluding Remarks
References
Glossary
Index