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Cognitive Radio Networks Optimization with Spectrum Sensing Algorithms.
In recent years, a considerable amount of effort has been devoted, both in industry and academia, towards the efficient utilization of the available spectrum under the various propagation models which lead towards the design and dimensioning of the future network Internet of Things (IoT).This book f...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Aalborg :
River Publishers,
2014.
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| Colección: | River Publishers series in communications.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Half Title
- Cognitive Radio Networks Optimization with Spectrum Sensing Algorithms; Seres in River Publishers; Title
- Cognitive Radio Networks Optimization with Spectrum Sensing Algorithms; Copyright; Contents; Preface; Acknowledgments; List of Figures; List of Tables; List of Abbreviations; Chapter_1 Novel Application of TV White Space; 1.1 Introduction; 1.2 DD: International Scenario; 1.3 Regulatory Framework in India; 1.4 DD: Indian Scenario; 1.4.1 Spectrum Allocation in India; 1.5 Joint Task Group and 700 MHz; 1.6 Opportunistic Spectrum Access in India; 1.7 Opportunities inTVWS.
- 1.7.1 Wide Area Coverage in Rural Areas (e.g. IEEE 802.22)1.7.2 Super Wi-Fi/Low-Power Broadband (e.g. IEEE 802.11af); 1.7.3 Broadcasting Services; 1.7.4 DVB-H with Cognitive Access toTVWS; 1.7.5 LTE Extension; 1.7.6 Femto Cell for Wireless Broadband inTVWS; 1.7.7 Public Safety Application; 1.8 Empowering Rural India; 1.8.1 E-Agriculture; 1.8.2 E-Animal Husbandry; 1.8.3 E-Health; 1.8.4 E-Education; 1.8.5 E-Governance; 1.9 Use Cases forTVWS Usage; 1.9.1 Use Case: Mid-/Long-Range Wireless Access; 1.9.1.1 Mid-/long-range: no mobility; 1.9.1.2 Mid-/long-range: low mobility.
- 1.9.1.3 Mid-/long range: high mobility1.9.1.4 Centralized network management; 1.9.2 Use Case: Short-Range Wireless Access; 1.9.2.1 Uncoordinated networks; 1.9.2.2 Coordinated networks; 1.9.2.3 Hybrid of uncoordinated and coordinated networks; 1.9.3 Use Case: Opportunistic Spectrum Access by CellularSystems; 1.9.4 Use Case: Ad Hoc Networking overWS Frequency Bands; 1.11 Regulatory Activities Related to CR andTVWS; 1.12 Conclusions; 1.10 QoS inTVWS; 1.10.1 High QoS; 1.10.2 Moderate QoS; References; Chapter_2 Spectrum Sensing in Cognitive Radio; 2.1 Introduction; 2.2 Dynamic Spectrum Access.
- 2.3 Cognitive Radio2.4 Spectrum Sensing Challenges; 2.4.1 Hidden Primary User Problem; 2.4.2 Channel Uncertainty; 2.4.3 Noise Uncertainty; 2.4.4 Cross-Layer Design; 2.4.5 Spread Spectrum Primary Users Detection; 2.4.6 Sensing Duration and Frequency; 2.4.7 Decision Fusion in Cooperative Sensing; 2.4.8 Security and Trusted Access; 2.4.9 Spectrum Sensing in Multidimensional Environment; 2.4.10 Interference Temperature Measurement; 2.4.11 Complexity Issue; 2.5 Characteristics of Spectrum Sensing; 2.6 Spectrum Sensing Methods; 2.6.1 Matched Filtering; 2.6.2 Cyclostationary Detection; 2.6.3 GLRT.
- 2.6.4 Multitaper Spectrum Estimation2.6.5 Wavelets; 2.6.6 Energy Detection; 2.6.7 Covariance-based Method; 2.6.8 Other Spectrum Sensing Methods; 2.7 Analysis of Energy Detection and CovarianceAbsolute Value Method; 2.7.1 Energy Detection; 2.7.2 Covariance Absolute Value; 2.7.2.1 Steps for obtaining the pre-whiten matrix; 2.7.2.2 Flow Chart for CAV method; 2.7.3 Simulation Results for ED without Noise Uncertaintyand CAV; 2.7.4 Simulation Results for ED with Noise Uncertainty and CAV; 2.8 Hybrid Detection Method; 2.8.1 Comparison between ED- and CAV-based Detection; Computational complexity.