Notations and Acronyms xiv
General Introduction 1
0.1 Motivation . . . . 2
0.2 Spectrum Sensing Challenges . . . . 3
0.3 Objectives and contributions . . . . 4
0.3.1 Objectives . . . . 4
0.3.2 Key Contributions . . . . 5
0.4 Thesis Outline . . . . 5
1 Introduction to Cognitive Radio 9 1.1 Wireless Communications . . . . 9
1.2 Cognitive Radio . . . . 11
1.2.1 Software Defined Radio to Cognitive Radio . . . . 11
1.2.2 Definitions of a cognitive radio . . . . 12
1.2.3 Functions and components of Cognitive Radio . . . . 13
1.2.4 Cognition Cycle . . . . 14
1.2.5 Cognitive Radio Networks Architecture . . . . 15
1.3 Dynamic Spectrum Access and Management . . . . 16
1.3.1 Dynamic Exclusive Use Model . . . . 17
1.3.2 Open Sharing Model . . . . 18
1.3.3 Hierarchical Access Model . . . . 18
1.4 Cognitive Radio Standardization . . . . 19
1.5 Cognitive Radio Applications . . . . 22
1.6 Conclusion . . . . 23
2 Spectrum Sensing for Cognitive Radio 25 2.1 Introduction . . . . 25
2.2 Overview of Spectrum Sensing Algorithms . . . . 26
2.3 Statistical Detection Techniques . . . . 28
2.3.1 Maximum A Posteriori Detection (MAP) . . . . 29
2.3.2 Maximum Likelihood Detection (ML) . . . . 29
2.3.3 The Neyman-Pearson Detection . . . . 29
2.4 Detection performance . . . . 30
2.5 Energy Detection Based Spectrum Sensing . . . . 31
2.5.1 Energy Detector . . . . 32
2.5.2 Noise Power Uncertainty in Energy Detection . . . . 37
2.6 Matched Filter Based Spectrum Sensing . . . . 38
2.7 Cyclostationary Based Spectrum Sensing . . . . 40
2.7.1 Cyclostationary Analysis . . . . 40
2.7.2 Cyclostationary Feature Detection for CR . . . . 42
2.7.3 Cyclostationary based spectrum sensing limitations . . . 43
2.8 Eigenvalue based Spectrum Sensing . . . . 44
2.8.1 Computation of the sample covariance matrix . . . . 45
2.8.2 Implementation of Maximum-Minimum Eigenvalues ratio detector (MME) . . . . 45
2.9 Spectrum Sensing Methods between strength and weakness . . 47
2.10 Other Spectrum Sensing Methods . . . . 47
2.10.1 Covariance Based Spectrum Sensing . . . . 48
2.10.2 Wavelet Based Spectrum Sensing . . . . 49
2.10.3 Filter Bank Based Spectrum Sensing . . . . 50
2.10.4 Multitaper Method Based Spectrum Sensing (MTM) . . . 51
2.10.5 High-order Statistics Based Spectrum Sensing . . . . 51
2.11 Cooperative Spectrum Sensing . . . . 52
2.12 Conclusion . . . . 54
3 Optimization of Centralized Cooperative Spectrum Sensing for Cog- nitive Radio Networks 55 3.1 Introduction . . . . 55
3.2 Related Works . . . . 58
3.3 Issues in Cooperative Spectrum Sensing . . . . 59
3.4 System Model . . . . 60
3.5 Fusion Rules . . . . 62
3.5.1 Hard fusion rules . . . . 62
3.5.2 Soft data fusion . . . . 64
3.5.3 Quantized data fusion . . . . 67
3.6 Cognitive Radio Transmission Scenarios . . . . 70
3.6.1 Combining Rules for CSS under CR Transmission Sce- narios . . . . 71
3.6.2 Performances detection of CSS under CPUP and CSUSU Transmission mode . . . . 76
3.7 Throughput Optimization for Cooperative Spectrum Sensing in CRN . . . . 79
3.7.1 Throughput Optimization under CR Transmission Sce- narios . . . . 79
3.7.2 Capacity Optimization detection for CSS under CPUP and CSUSU Transmission mode . . . . 80
3.8 Conclusion . . . . 83
4 Blind Spectrum Sensing Based on Statistic test (GoF test) 85 4.1 Introduction . . . . 85
4.2 Goodness of Fit Tests . . . . 86
4.3 Spectrum Sensing method based on GoF test using chi-square distribution . . . . 88
4.3.1 Performance comparison of existing GoF sensing methods 90 4.4 Adaptation of existing GoF tests for spectrum sensing . . . . 92
4.4.1 Modified AD GoF sensing . . . . 92
4.4.2 Chi-square GoF test for spectrum sensing . . . . 94
4.4.3 Order Statistic (OS) GoF sensing method . . . . 96
4.5 Spectrum Sensing Based on The Likelihood Ratio Goodness of
Fit test . . . . 98
4.5.1 Likelihood based Goodness of fit test . . . . 99
4.5.2 The proposed spectrum sensing (LLR-GoF sensing) . . . 100
4.6 GoF Sensing Under Non Gaussian Noise and Noise Uncertainty 102 4.6.1 Non Gaussian noise (GM Model) . . . 102
4.6.2 Noise uncertainty . . . 106
4.7 New proposed GoF sensing method . . . 110
4.7.1 AD sensing method based on sub-blocks . . . 110
4.7.2 Spectrum Sensing Method Based on The new GoF statis- tic test . . . 112
4.8 Wide-band Spectrum Sensing based on GoF testing . . . 119
4.8.1 Result on Synthetic Data . . . 120
4.9 Conclusion . . . 123
5 Distributed Consensus Spectrum Sensing For CRN 125 5.1 Introduction . . . 125
5.2 Related Works . . . 126
5.3 Network Model for Distributed Spectrum sensing . . . 128
5.4 Spectrum sensing Model . . . 128
5.5 The Consensus Algorithms for Distributed Spectrum Sensing . 131 5.6 Weighted Average Consensus for Distributed Spectrum Sensing 133 5.7 Test the optimality of the proposed weighted consensus DSS scheme . . . 137
5.7.1 Exhaustive Search (ES) based algorithm . . . 138
5.7.2 GA based GoF cooperative spectrum sensing . . . 139
5.7.3 Simulation results and comparison . . . 140
5.8 Conclusion . . . 143
6 Conclusions and Future Work 145 6.1 Conclusions . . . 145
6.2 Future Work . . . 147
List of Publications 149
Bibliography 151
