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E-raamat: Wavelet Radio: Adaptive and Reconfigurable Wireless Systems Based on Wavelets

(Technische Universiteit Delft, The Netherlands)
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"Adaptive and Reconfigurable Wireless Systems Based on Wavelets The first book to provide a detailed discussion of the application of wavelets in wireless communications, this is an invaluable source of information for graduate students, researchers, andtelecommunications engineers, managers and strategists. It overviews applications, explains how to design new wavelets and compares wavelet technology with existing OFDM technology"--

"The first book to provide a detailed discussion of the application of wavelets in wireless communications, this is an invaluable source of information for graduate students, researchers, and telecommunications engineers, managers and strategists. It overviews applications, explains how to design new wavelets and compares wavelet technology with existing OFDM technology. [ bullet] Addresses the applications and challenges of wavelet technology for a range of wireless communication domains [ bullet] Aids inthe understanding of Wavelet Packet Modulation and compares it with OFDM [ bullet] Includes tutorials on convex optimisation, spectral factorisation and the design of wavelets [ bullet] Explains design methods for new wavelet technologies for wireless communications, addressing many challenges, such as peak-to-average power ratio reduction, interference mitigation, reduction of sensitivity to time, frequency and phase offsets, and efficient usage of wireless resources [ bullet] Describes the application of wavelet radio in spectrum sensing of cognitive radio systems"--

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Thorough description of the theory, applications and design methods of wavelets in communications systems.
Preface ix
Acknowledgement xi
1 Introduction
1(10)
1.1 Background
1(2)
1.1.1 The need
2(1)
1.1.2 The means
3(1)
1.2 Wavelet transform as a tool for wireless communications
3(5)
1.2.1 Wavelets and wavelet transform
3(1)
1.2.2 Advantages of wavelet transform for wireless communication
4(2)
1.2.3 Application of wavelets for wireless transmission
6(1)
1.2.4 Wavelet-packet-based multi-carrier modulation (WPM) system
6(2)
1.3 Scope of the book
8(3)
1.3.1 Theoretical background (Chapters 1 and 2)
8(1)
1.3.2 Wavelet radio (Chapters 3, 4 and 5)
8(1)
1.3.3 Wavelet applications in cognitive radio design (Chapters 6 and 7)
9(2)
2 Theory of wavelets
11(24)
2.1 Introduction
12(2)
2.1.1 Representation of signals
12(1)
2.1.2 Fourier analysis
13(1)
2.1.3 Gabor transform
13(1)
2.1.4 Wavelet analysis
14(1)
2.2 Continuous wavelet transform
14(4)
2.2.1 Orthonormal wavelets
17(1)
2.2.2 Non-dyadic wavelets
18(1)
2.3 Multi-resolution analysis
18(2)
2.4 Discrete wavelet transform
20(1)
2.5 Filter bank representation of DWT
21(5)
2.5.1 Analysis filter bank
21(3)
2.5.2 Synthesis filter bank
24(2)
2.6 Wavelet packet transform
26(2)
2.7 Wavelet types
28(5)
2.7.1 Wavelet properties
29(3)
2.7.2 Popular wavelet families
32(1)
2.8 Summary
33(2)
3 Wavelet packet modulator
35(20)
3.1 Modulation techniques for wireless communication
36(2)
3.1.1 Single-carrier transmission
36(2)
3.2 Orthogonal frequency division multiplexing
38(3)
3.3 Filter bank multi-carrier methods
41(4)
3.3.1 Filtered multi-tone (FMT)
42(1)
3.3.2 Cosine modulated multi-tone (CMT)
43(1)
3.3.3 OFDM-offset QAM/staggered multi-tone (SMT)
44(1)
3.4 Wavelet and wavelet-packet-based multi-carrier modulators
45(7)
3.4.1 Wavelet packet modulator (WPM)
45(3)
3.4.2 Variants of wavelet packet modulator
48(3)
3.4.3 Interpolated tree orthogonal multiplexing (ITOM)
51(1)
3.5 Summary
52(3)
4 Synchronization issues of wavelet radio
55(38)
4.1 Introduction
55(1)
4.2 Frequency offset in multi-carrier modulation
56(9)
4.2.1 Modelling frequency offset errors
56(1)
4.2.2 Frequency offset in OFDM
57(1)
4.2.3 Frequency offset in WPM
58(1)
4.2.4 Numerical results for frequency offset
59(6)
4.3 Phase noise in multi-carrier modulation
65(11)
4.3.1 Modelling the phase noise
66(1)
4.3.2 Phase noise in OFDM
67(1)
4.3.3 Phase noise in WPM
68(2)
4.3.4 Numerical results for phase noise
70(6)
4.4 Time offset in multi-carrier modulation
76(14)
4.4.1 Modelling time offset errors
76(2)
4.4.2 Time offset in OFDM
78(2)
4.4.3 Time synchronization error in WPM
80(1)
4.4.4 Modulation scheme
81(1)
4.4.5 Numerical results for time offset
82(8)
4.5 Summary
90(3)
5 Peak-to-average power ratio
93(19)
5.1 Background
93(1)
5.2 Introduction
93(1)
5.3 PAPR distribution of multi-carrier signal
94(5)
5.3.1 Ofdm
94(1)
5.3.2 Wpm
95(4)
5.4 PAPR reduction techniques
99(4)
5.4.1 Signal-scrambling techniques
100(1)
5.4.2 Signal-distortion techniques
101(1)
5.4.3 Criteria for selection of PAPR reduction technique
102(1)
5.5 Selected mapping with phase modification
103(6)
5.5.1 Description of algorithm
103(2)
5.5.2 Numerical results
105(4)
5.6 Summary
109(3)
6 Wavelets for spectrum sensing in cognitive radio applications
112(27)
6.1 Background
112(1)
6.2 Spectrum sensing in cognitive radio
112(2)
6.3 Spectrum sensing methods
114(2)
6.3.1 Periodogram
114(1)
6.3.2 Correlogram
115(1)
6.4 Advantages and disadvantages of conventional spectrum sensing techniques in cognitive radio
116(4)
6.4.1 Pilot detection via matched filtering
116(1)
6.4.2 Energy detection
116(1)
6.4.3 Cyclostationary feature detection
116(1)
6.4.4 Multi-taper spectrum estimation (MTSE)
117(2)
6.4.5 Filter bank spectrum estimation (FBSE)
119(1)
6.5 Advantages of wavelets in spectrum estimation
120(1)
6.6 Performance evaluation of spectrum sensing in cognitive radio
121(2)
6.6.1 Basic principle of energy detector
121(1)
6.6.2 Evaluation of receiver operating characteristic (ROC)
122(1)
6.7 Wavelet packet spectrum estimator (WPSE)
123(6)
6.7.1 Evaluation of ROC performance of WPSE
125(4)
6.8 An efficient model of wavelet-packet based spectrum estimator
129(3)
6.8.1 WPSE model
129(2)
6.8.2 Study of the detection performance of the developed model
131(1)
6.9 Wavelet-packet-based spectrum estimator (WPSE) and compressed sensing
132(4)
6.9.1 Introduction to compressed sensing
132(1)
6.9.2 Compressed sensing and WPSE
133(3)
6.10 Summary
136(3)
7 Optimal wavelet design for wireless communications
139(41)
7.1 Introduction
139(1)
7.2 Criteria for design of wavelets
140(4)
7.2.1 Design procedure
140(1)
7.2.2 Filter bank implementation of WPM
141(1)
7.2.3 Important wavelet properties
141(3)
7.2.4 Degrees of freedom to design
144(1)
7.3 Example 1 - Maximally frequency selective wavelets
144(18)
7.3.1 Formulation of design problem
146(1)
7.3.2 Transformation of non-convex problem to linear/convex problem
147(4)
7.3.3 Reformulation of optimization problem in the Q(a>) function domain
151(3)
7.3.4 Solving the convex optimization problem
154(1)
7.3.5 Results and analysis
154(8)
7.4 Example 2 - Wavelets with low cross-correlation error
162(15)
7.4.1 Time offset errors in WPM
165(1)
7.4.2 Formulation of design problem
165(2)
7.4.3 Transformation of the mathematical constraints from a non-convex problem to a convex/linear one
167(1)
7.4.4 Results and analysis
168(9)
7.5 Summary
177(3)
8 Conclusion
180(13)
8.1 Study of wavelet radio performance under loss of synchronization
182(1)
8.2 PAPR performance studies
183(1)
8.3 Wavelet-based spectrum sensing for cognitive radio
183(1)
8.4 Design of wavelets
184(1)
8.5 Future research topics
185(2)
8.5.1 Study of WPM performance under loss of synchronization
185(1)
8.5.2 PAPR performance studies
185(1)
8.5.3 Equalization of channel
186(1)
8.5.4 Wavelet packet spectrum estimator (WPSE)
186(1)
8.5.5 Design of wavelets
187(1)
8.6 Related studies
187(1)
8.7 Beyond this book
188(3)
8.7.1 Wavelet-based modelling of time-variant wireless channels
188(1)
8.7.2 Multiple-access communication
189(1)
8.7.3 Wavelet radio for green communication
189(1)
8.7.4 Wavelet-based multiple-input-multiple-output communications (MIMO)
190(1)
8.8 Concluding remarks
191(2)
Appendix 1 Semi-definitive programming 193(1)
Appendix 2 Spectral factorization 194(1)
Appendix 3 Sum of squares of cross-correlation 195(1)
Index 196
Homayoun Nikookar is an Associate Professor in the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology where he leads the Radio Advanced Technologies and Systems (RATS) programme and supervises a team of researchers carrying out cutting-edge research in the field of advanced radio transmission. He has received several paper awards at international conferences and symposiums and the 'Supervisor of the Year Award' at Delft University in 2010.
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