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Spectrum Sensing for Cognitive Radio: Fundamentals and Applications [Hardback]

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(SVNIT, India), (DAIICT, India)
  • Formāts: Hardback, 230 pages, height x width: 234x156 mm, weight: 489 g, 25 Tables, black and white; 78 Line drawings, black and white; 78 Illustrations, black and white
  • Izdošanas datums: 05-Jan-2022
  • Izdevniecība: CRC Press
  • ISBN-10: 0367542935
  • ISBN-13: 9780367542931
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Spectrum Sensing for Cognitive Radio: Fundamentals and ApplicationsPalielināt
  • Formāts: Hardback, 230 pages, height x width: 234x156 mm, weight: 489 g, 25 Tables, black and white; 78 Line drawings, black and white; 78 Illustrations, black and white
  • Izdošanas datums: 05-Jan-2022
  • Izdevniecība: CRC Press
  • ISBN-10: 0367542935
  • ISBN-13: 9780367542931
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780367542931.html
Keywords:
This comprehensive reference text discusses concepts of cognitive radio and the advances in the field of spectrum sensing.

This text discusses the concept of cognitive radio for next generation wireless communication and a very critical aspect of cognitive radio that is, spectrum sensing in detail. It covers important topics including narrowband spectrum sensing, wideband spectrum sensing, cooperative spectrum sensing, system and channel models, detection algorithms, approximation of decision statistics, and theoretical analysis of detection algorithms in detail. Separate chapters are dedicated to discussing the analysis and use of detection algorithms for narrowband spectrum sensing, wideband spectrum sensing, and cooperative wideband spectrum sensing.

Aimed at graduate students and academic researchers in the fields of electrical engineering and electronics and communication engineering, this text:











Discusses concepts of cognitive radio and research in spectrum sensing.





Presents mathematical analysis of algorithms considering practical environment.





Explains novel wideband spectrum sensing algorithms with detailed analysis.





Provides mathematical derivations to help readers.





Discusses basic spectrum sensing algorithms, from narrowband spectrum sensing to the more advanced wideband spectrum sensing.
Preface xiii
Contributors xv
List of Figures xvii
List of Tables xxi
Acronyms xxiii
Acknowledgments xxv
Chapter 1 Fundamentals of Probability Theory
1(36)
1.1 Introduction
1(1)
1.2 Basics of Probability
2(5)
1.2.1 Probability of an Event
4(2)
1.2.1.1 Axiomatic Definition
4(1)
1.2.1.2 Relative Frequency Definition
5(1)
1.2.1.3 Classical Definition
5(1)
1.2.2 Conditional Probability
6(1)
1.2.3 Independence of Events
7(1)
1.3 Random Variable
7(18)
1.3.1 Cumulative Distribution Function (CDF)
8(1)
1.3.1.1 Properties of Cumulative Distribution Function
8(1)
1.3.2 The Probability Density Function (PDF)
9(1)
1.3.2.1 Properties of Probability Density Functions
9(1)
1.3.3 Joint Distribution and Density Function
9(1)
1.3.4 Conditional Probability Density Function
10(1)
1.3.5 Statistical Independence
11(1)
1.3.6 Moments of a Random Variable
11(3)
1.3.7 Some Key Random Variables
14(8)
1.3.7.1 Discrete Random Variables
14(1)
1.3.7.2 Continuous Random Variables
15(7)
1.3.8 The Markov and Chebyschev Inequalities
22(1)
1.3.9 The Sample Mean and the Laws of Large Numbers
22(2)
1.3.9.1 Weak Law of Large Numbers
24(1)
1.3.9.2 Strong Law of Large Numbers
24(1)
1.3.10 Central Limit Theorem (CLT)
24(1)
1.4 Stochastic Process
25(12)
1.4.1 Definition of Stochastic Process
25(1)
1.4.2 Statistics of Stochastic Process
26(2)
1.4.3 Stationarity
28(2)
1.4.3.1 Properties of Autocorrelation Function
29(1)
1.4.4 Random Process through Linear System
30(2)
1.4.5 Power Spectral Density (PSD)
32(2)
1.4.5.1 Properties of Power Spectral Density
33(1)
1.4.5.2 Output Spectral Density of an LTI System
34(1)
1.4.6 Gaussian Random Process
34(1)
1.4.7 White Noise
35(2)
Chapter 2 Introduction
37(18)
2.1 Cognitive Radio
38(5)
2.2 Spectrum Sensing
43(9)
2.2.1 Narrowband Spectrum Sensing
43(4)
2.2.1.1 Matched Filter Detection
44(1)
2.2.1.2 Cyclostationary Detection
45(1)
2.2.1.3 Covariance-Based Detection
46(1)
2.2.1.4 Eigenvalue-Based Detection
46(1)
2.2.1.5 Energy Detection
46(1)
2.2.2 Wideband Spectrum Sensing
47(2)
2.2.2.1 Nyquist Wideband Spectrum Sensing
48(1)
2.2.2.2 Sub-Nyquist Wideband Spectrum Sensing
48(1)
2.2.3 Cooperative Spectrum Sensing
49(2)
2.2.4 Machine-Learning-Based Spectrum Sensing
51(1)
2.3 Book Contributions
52(1)
2.4 Tour of the Book
53(2)
Chapter 3 Literature Review
55(12)
3.1 Narrowband Spectrum Sensing
55(3)
3.2 Wideband Spectrum Sensing
58(2)
3.3 Cooperative Spectrum Sensing
60(2)
3.4 Machine-Learning-Based Spectrum Sensing
62(5)
Part I Narrowband Spectrum Sensing
Chapter 4 Energy-Detection-Based Spectrum Sensing over Generalized Fading Model
67(16)
4.1 System and Channel Models
68(2)
4.1.1 Energy Detection (ED)
68(2)
4.1.2 ηλμ Fading Model
70(1)
4.2 Average Probability of Detection over ηλμ Fading Channel
70(6)
4.2.1 No Diversity
70(2)
4.2.2 Square Law Selection (SLS) Diversity
72(2)
4.2.3 Cooperative Spectrum Sensing
74(2)
4.3 Average Probability of Detection over Channels with ηλμ Fading and Shadowing
76(1)
4.4 Results and Discussion
77(5)
4.5 Conclusion
82(1)
Chapter 5 Generalized Energy Detector in the Presence of Noise Uncertainty and Fading
83(38)
5.1 System Model
84(1)
5.2 Noise Uncertainty Model
85(1)
5.3 SNR Wall for AWGN Channel
86(17)
5.3.1 No Diversity
87(2)
5.3.2 pLC Diversity
89(7)
5.3.3 pLS Diversity
96(2)
5.3.4 CSS with Hard Combining
98(4)
5.3.4.1 OR Rule
99(1)
5.3.4.2 AND Rule
100(1)
5.3.4.3 k Out of M Combining Rule
101(1)
5.3.5 CSS with Soft Combining
102(1)
5.4 SNR Wall for Fading Channel
103(5)
5.4.1 No Diversity
103(2)
5.4.2 pLC Diversity
105(1)
5.4.3 pLS Diversity
106(1)
5.4.4 CSS with Hard Combining
106(2)
5.4.4.1 OR Combining
107(1)
5.4.4.2 AND Combining
107(1)
5.4.5 CSS with Soft Combining
108(1)
5.5 Results and Discussion
108(9)
5.5.1 SNR Wall for AWGN Case
109(3)
5.5.2 SNR Wall for Fading Case
112(2)
5.5.3 Effect of Noise Uncertainty and Fading on Detection Performance
114(2)
5.5.4 Effect of p
116(1)
5.6 Conclusion
117(4)
Part 2 Wideband Spectrum Sensing
Chapter 6 Diversity for Wideband Spectrum Sensing under Fading
121(32)
6.1 System Model and Performance Metrics
122(2)
6.2 Detection Algorithms
124(5)
6.2.1 Channel-by-Channel Square Law Combining (CC-SLC)
125(1)
6.2.2 Ranked Square Law Combining (R-SLC) Detection
126(1)
6.2.3 Ranked Square Law Selection (R-SLS) Detection
127(2)
6.3 Approximation of Decision Statistic
129(4)
6.3.1 PDF for SLC Diversity
130(2)
6.3.1.1 Without Using Approximation
130(1)
6.3.1.2 Using Approximation
130(2)
6.3.2 PDF for SLS Diversity
132(1)
6.3.2.1 Without Using Approximation
132(1)
6.3.2.2 Using Approximation
133(1)
6.4 Theoretical Analysis of Detection Algorithms
133(6)
6.4.1 Channel-by-Channel Square Law Combining (CC-SLC)
133(2)
6.4.2 Theoretical Analysis for R-SLC
135(3)
6.4.3 Theoretical Analysis of R-SLS
138(1)
6.5 Results and Discussion
139(12)
6.6 Conclusion
151(2)
Chapter 7 Cooperative Wideband Spectrum Sensing
153(26)
7.1 System Model and Performance Metrics
154(1)
7.2 Proposed CWSS Algorithms
155(4)
7.2.1 Proposed Algorithm Based on Hard Combining
155(2)
7.2.2 Proposed Algorithm Based on Soft Combining
157(2)
7.3 Approximation to pdf of Decision Statistic
159(3)
7.4 Theoretical Analysis of the Detection Algorithms
162(6)
7.4.1 Theoretical Analysis for Algorithm 4
162(3)
7.4.1.1 Performance Using Any Value of M with Fixed L
164(1)
7.4.1.2 Performance Using Any Value of L with Fixed M
165(1)
7.4.2 Theoretical Analysis for Algorithm 5
165(3)
7.5 Results and Discussion
168(10)
7.5.1 Experimentations Using Algorithm 4
168(6)
7.5.2 Experimentations Using Algorithm 5
174(4)
7.6 Conclusion
178(1)
Chapter 8 Conclusions and Future Research Directions
179(4)
8.1 Conclusions
179(1)
8.2 Future Research Directions
180(3)
Appendix A Appendix for
Chapter 1		 183(4)
A.1 Proof for Markov Inequality
183(1)
A.2 Proof Central Limit Theorem
184(1)
A.3 Characteristic Function of Gaussian Random Variable
185(2)
Appendix B Appendix for
Chapter 4		 187(2)
B.1 Derivation for PF (τ) in Eq. (4.3)
187(2)
Appendix C Appendix for
Chapter 5		 189(6)
C.1 Derivation for PD,plc in Eq. (5.27)
189(3)
C.2 Derivation for PDNak in Eq. (5.77)
192(1)
C.3 Derivation for PDNak,plc in Eq. (5.81)
193(2)
Appendix D Appendix for
Chapter 6		 195(8)
D.1 Proof for Convergence of PDF of SLC under Nakagami Fading Channel in Eq. (6.12)
195(1)
D.2 Derivation of PDF of SLS under Nakagami Fading in Eq. (6.17)
195(1)
D.3 Proof for Convergence of PDF of SLS under Nakagami Fading in Eq. (6.17)
196(1)
D.4 Derivation of PDF in Eq. (6.13)
197(1)
D.5 Derivation of Eq. (6.34)
198(1)
D.6 Derivation of PDF in Eq. (6.36)
199(1)
D.7 Theoretical Analysis of R-SLC for L = 3
199(4)
Appendix E Some Special Functions 203(6)
E.1 Gamma Function
203(1)
E.2 Lower Incomplete Gamma Function
203(1)
E.3 Upper Incomplete Gamma Function
204(1)
E.4 Generalized Marcum Q-Function
204(1)
E.5 Bessel Function of the First Kind
204(1)
E.6 Modified Bessel Function of the First Kind
205(1)
E.7 Confluent Hypergeometric Function
205(1)
E.8 Confluent Hypergeometric Function of the Second Kind
206(1)
E.9 Unit Step Function
206(1)
E.10 Q-Function
206(1)
E.11 Error Function
207(1)
E.12 Polylogarithm
207(2)
Bibliography 209(18)
Index 227
KAMAL M. CAPTAIN: Dr. Kamal M. Captain received his Ph.D. in the area of Spectrum Sensing for Cognitive Radio from Dhirubhai Ambani Institute of Information and Communication Technology (DAIICT), Gandhinagar, India. Before this, he completed his M.Tech in Communication Systems from Sardar Vallabhbhai National Institute of Technology (SVNIT) and B.E degree in Electronics and Communication Engineering from Veer Narmad South Gujarat University (VNSGU), Surat, Gujarat, India. He is currently serving as an assistant professor at Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, Gujarat, India. Prior to joining SVNIT, he served as a senior engineer (signal processing) at eInfochips, Ahmedabad, India. He has been involved in active research in the areas of cognitive radio, wireless communication, signal processing, and machine learning and has several journals and international conference papers, including IEEE Transactions. He has also served as a reviewer for IEEE Transactions, letters, and top tier conferences.

MANJUNATH V. JOSHI: Prof. Manjunath V. Joshi received the B.E. degree from the University of Mysore, Mysore, India, and the M.Tech. and Ph.D. degrees from the Indian Institute of Technology Bombay (IIT Bombay), Mumbai, India. He is currently serving as a Professor and and Dean Research and Development with the Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India. He has been involved in active research in the areas of Signal and Image Processing, Cognitive Radio, Computer Vision, and Machine Learning and has several publications in quality journals and conferences. He has co-authored four books entitled Motion-Free Super Resolution (Springer, New York-2005), Digital Heritage Reconstruction using Super-resolution and Inpainting (Morgan and Claypool-2016), Regularization in Hyperspectral Unmixing (SPIE Press-2016), and the book entitled Multi-resolution Image Fusion in Remote Sensing (Cambridge University Press, UK-2019). Currently, he is contributing as a co-author of a book to be published by Springer in remote sensing, where seven positive reviews have been received. So far, nine Ph.D. students have been graduated under his supervision. Dr. Joshi was a recipient of the Outstanding Researcher Award in Engineering Section by IIT Bombay in 2005 and the Dr. Vikram Sarabhai Award for 2006-2007 of information technology constituted by the Government of Gujarat, India. He served as a Program Co-Chair for the 3rd ACCVWorkshop on E-Heritage, 2014, held in Singapore. He has also served as Visiting Professor at IIT Gandhinagar and IIIT Vadodara. He has visited Germany, Italy, France, Hong Kong, the USA, Canada, South Korea, Indonesia and contributed to research in his area of expertise.