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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Dhope, Tanuja S.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Aalborg : River Publishers, 2014.
Colección:River Publishers series in communications.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Dhope, Tanuja S. 
245 1 0 |a Cognitive Radio Networks Optimization with Spectrum Sensing Algorithms. 
260 |a Aalborg :  |b River Publishers,  |c 2014. 
300 |a 1 online resource (191 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a River Publishers Series in Communications 
588 0 |a Print version record. 
505 0 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
500 |a 2.8.2 Hybrid Detection Method. 
520 |a 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 focuses on Television White Space (TVWS) opportunities and regulatory aspects for cognitive radio applications, and includes case studies for the exploitation of TVWS depending on user's mobility, and the geo-location between user and the Base Station. The book presents recent advances in spectrum sensing, reflecting state of the art technology and research achievements in this area as well as a new insights in spectrum sensing of performance modeling, analysis and worldwide applications. Technical topics discussed include: Novel Application of TV White Space Spectrum Sensing in Cognitive Radio Cooperative Spectrum Sensing DoA Estimation Algorithms 
545 0 |a Tanuja S. Dhope 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Cognitive radio networks. 
650 0 |a Cognitive radio networks  |x Mathematical models. 
650 6 |a Radio cognitive. 
650 6 |a Radio cognitive  |x Modèles mathématiques. 
650 7 |a SCIENCE / Energy  |2 bisacsh 
650 7 |a Cognitive radio networks  |2 fast 
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776 0 8 |i Print version:  |a Dhope, Tanuja S.  |t Cognitive Radio Networks Optimization with Spectrum Sensing Algorithms.  |d Aalborg : River Publishers, ©2014  |z 9788793102002 
830 0 |a River Publishers series in communications. 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4509460  |z Texto completo 
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