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Spectrum Sensing for Cognitive Radio: Fundamentals and Applications 2021 book

Spectrum Sensing for Cognitive Radio: Fundamentals and Applications

Details Of The Book

Spectrum Sensing for Cognitive Radio: Fundamentals and Applications

edition: 1 
Authors: ,   
ISBN : 0367542935, 9780367542931 
publisher: CRC Press 
publish year: 2021 
pages: 256 
language: English 
ebook format : PDF (It will be converted to PDF, EPUB OR AZW3 if requested by the user) 
file size: 8 MB 

price : $11.85 15 With 21% OFF

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Abstract Of The Book

Table Of Contents

Half Title
Title Page
Copyright Page
List of Figures
List of Tables
Chapter 1: Fundamentals of Probability Theory
	1.1. Introduction
	1.2. Basics of Probability
		1.2.1. Probability of an Event Axiomatic Definition Relative Frequency Definition Classical Definition
		1.2.2. Conditional Probability
		1.2.3. Independence of Events
	1.3. Random Variable
		1.3.1. Cumulative Distribution Function (CDF) Properties of Cumulative Distribution Function
		1.3.2. The Probability Density Function (PDF) Properties of Probability Density Functions
		1.3.3. Joint Distribution and Density Function
		1.3.4. Conditional Probability Density Function
		1.3.5. Statistical Independence
		1.3.6. Moments of a Random Variable
		1.3.7. Some Key Random Variables Discrete Random Variables Continuous Random Variables
		1.3.8. The Markov and Chebyschev Inequalities
		1.3.9. The Sample Mean and the Laws of Large Numbers Weak Law of Large Numbers Strong Law of Large Numbers
		1.3.10. Central Limit Theorem (CLT)
	1.4. Stochastic Process
		1.4.1. Definition of Stochastic Process
		1.4.2. Statistics of Stochastic Process
		1.4.3. Stationarity Properties of Autocorrelation Function
		1.4.4. Random Process through Linear System
		1.4.5. Power Spectral Density (PSD) Properties of Power Spectral Density Output Spectral Density of an LTI System
		1.4.6. Gaussian Random Process
		1.4.7. White Noise
Chapter 2: Introduction
	2.1. Cognitive Radio
	2.2. Spectrum Sensing
		2.2.1. Narrowband Spectrum Sensing Matched Filter Detection Cyclostationary Detection Covariance-Based Detection Eigenvalue-Based Detection Energy Detection
		2.2.2. Wideband Spectrum Sensing Nyquist Wideband Spectrum Sensing Sub-Nyquist Wideband Spectrum Sensing
		2.2.3. Cooperative Spectrum Sensing
		2.2.4. Machine-Learning-Based Spectrum Sensing
	2.3. Book Contributions
	2.4. Tour of the Book
Chapter 3: Literature Review
	3.1. Narrowband Spectrum Sensing
	3.2. Wideband Spectrum Sensing
	3.3. Cooperative Spectrum Sensing
	3.4. Machine-Learning-Based Spectrum Sensing
PART I: Narrowband Spectrum Sensing
	Chapter 4: Energy-Detection-Based Spectrum Sensing over Generalized Fading Model
		4.1. System and Channel Models
			4.1.1. Energy Detection (ED)
			4.1.2. ƞ-λ-µ Fading Model
		4.2. Average Probability of Detection over ƞ-λ-µ Fading Channel
			4.2.1. No Diversity
			4.2.2. Square Law Selection (SLS) Diversity
			4.2.3. Cooperative Spectrum Sensing
		4.3. Average Probability of Detection over Channels with ƞ-λ-µ Fading and Shadowing
		4.4. Results and Discussion
		4.5. Conclusion
	Chapter 5: Generalized Energy Detector in the Presence of Noise Uncertainty and Fading
		5.1. System Model
		5.2. Noise Uncertainty Model
		5.3. SNR Wall for AWGN Channel
			5.3.1. No Diversity
			5.3.2. pLC Diversity
			5.3.3. pLS Diversity
			5.3.4. CSS with Hard Combining OR Rule AND Rule k Out of M Combining Rule
			5.3.5. CSS with Soft Combining
		5.4. SNR Wall for Fading Channel
			5.4.1. No Diversity
			5.4.2. pLC Diversity
			5.4.3. pLS Diversity
			5.4.4. CSS with Hard Combining OR Combining AND Combining
			5.4.5. CSS with Soft Combining
		5.5. Results and Discussion
			5.5.1. SNR Wall for AWGN Case
			5.5.2. SNR Wall for Fading Case
			5.5.3. Effect of Noise Uncertainty and Fading on Detection Performance
			5.5.4. Effect of p
		5.6. Conclusion
PART II: Wideband Spectrum Sensing
	Chapter 6: Diversity for Wideband Spectrum Sensing under Fading
		6.1. System Model and Performance Metrics
		6.2. Detection Algorithms
			6.2.1. Channel-by-Channel Square Law Combining (CC-SLC)
			6.2.2. Ranked Square Law Combining (R-SLC) Detection
			6.2.3. Ranked Square Law Selection (R-SLS) Detection
		6.3. Approximation of Decision Statistic
			6.3.1. PDF for SLC Diversity Without Using Approximation Using Approximation Using Approximation Without Using Approximation Using Approximation
		6.4. Theoretical Analysis of Detection Algorithms
			6.4.1. Channel-by-Channel Square Law Combining (CC-SLC)
			6.4.2. Theoretical Analysis for R-SLC
			6.4.3. Theoretical Analysis of R-SLS
		6.5. Results and Discussion
		6.6. Conclusion
	Chapter 7: Cooperative Wideband Spectrum Sensing
		7.1. System Model and Performance Metrics
		7.2. Proposed CWSS Algorithms
			7.2.1. Proposed Algorithm Based on Hard Combining
			7.2.2. Proposed Algorithm Based on Soft Combining
		7.3. Approximation to pdf of Decision Statistic
		7.4. Theoretical Analysis of the Detection Algorithms
			7.4.1. Theoretical Analysis for Algorithm 4 Performance Using Any Value of M with Fixed L Performance Using Any Value of L with Fixed M
			7.4.2. Theoretical Analysis for Algorithm 5
		7.5. Results and Discussion
			7.5.1. Experimentations Using Algorithm 4
			7.5.2. Experimentations Using Algorithm 5
		7.6. Conclusion
	Chapter 8: Conclusions and Future Research Directions
		8.1. Conclusions
		8.2. Future Research Directions
Appendix A: Appendix for Chapter 1
	A.1. Proof for Markov Inequality
	A.2. Proof Central Limit Theorem
	A.3. Characteristic Function of Gaussian Random Variable
Appendix B: Appendix for Chapter 4
	B.1. Derivation for PF (t) in Eq. (4.3)
Appendix C: Appendix for Chapter 5
	C.1. Derivation for ¯PD,plc in Eq. (5.27)
	C.2. Derivation for ¯PNak D in Eq. (5.77)
	C.3. Derivation for ¯PNak D,plc in Eq. (5.81)
Appendix D: Appendix for Chapter 6
	D.1. Proof for Convergence of PDF of SLC under Nakagami Fading Channel in Eq. (6.12)
	D.2. Derivation of PDF of SLS under Nakagami Fading in Eq. (6.17)
	D.3. Proof for Convergence of PDF of SLS under Nakagami Fading in Eq. (6.17)
	D.4. Derivation of PDF in Eq. (6.13)
	D.5. Derivation of Eq. (6.34)
	D.6. Derivation of PDF in Eq. (6.36)
	D.7. Theoretical Analysis of R-SLC for L = 3
Appendix E: Some Special Functions
	E.1. Gamma Function
	E.2. Lower Incomplete Gamma Function
	E.3. Upper Incomplete Gamma Function
	E.4. Generalized Marcum Q-Function
	E.5. Bessel Function of the First Kind
	E.6. Modified Bessel Function of the First Kind
	E.7. Confluent Hypergeometric Function
	E.8. Confluent Hypergeometric Function of the Second Kind
	E.9. Unit Step Function
	E.10. Q-Function
	E.11. Error Function
	E.12. Polylogarithm

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