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Wavelet Radio

Book Description

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. Addresses the applications and challenges of wavelet technology for a range of wireless communication domains. Aids in the understanding of Wavelet Packet Modulation and compares it with OFDM. Includes tutorials on convex optimisation, spectral factorisation and the design of wavelets. 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. Describes the application of wavelet radio in spectrum sensing of cognitive radio systems.

Table of Contents

  1. Coverpage
  2. Wavelet Radio
  3. EuMA High Frequency Technologies Series
  4. Title page
  5. Copyright page
  6. Contents
  7. Preface
  8. Acknowledgement
  9. 1 Introduction
    1. 1.1 Background
      1. 1.1.1 The need
      2. 1.1.2 The means
    2. 1.2 Wavelet transform as a tool for wireless communications
      1. 1.2.1 Wavelets and wavelet transform
      2. 1.2.2 Advantages of wavelet transform for wireless communication
      3. 1.2.3 Application of wavelets for wireless transmission
      4. 1.2.4 Wavelet-packet-based multi-carrier modulation (WPM) system
    3. 1.3 Scope of the book
      1. 1.3.1 Theoretical background (Chapters 1 and 2)
      2. 1.3.2 Wavelet radio (Chapters 3, 4 and 5)
      3. 1.3.3 Wavelet applications in cognitive radio design (Chapters 6 and 7)
  10. 2 Theory of wavelets
    1. 2.1 Introduction
      1. 2.1.1 Representation of signals
      2. 2.1.2 Fourier analysis
      3. 2.1.3 Gabor transform
      4. 2.1.4 Wavelet analysis
    2. 2.2 Continuous wavelet transform
      1. 2.2.1 Orthonormal wavelets
      2. 2.2.2 Non-dyadic wavelets
    3. 2.3 Multi-resolution analysis
    4. 2.4 Discrete wavelet transform
    5. 2.5 Filter bank representation of DWT
      1. 2.5.1 Analysis filter bank
      2. 2.5.2 Synthesis filter bank
    6. 2.6 Wavelet packet transform
    7. 2.7 Wavelet types
      1. 2.7.1 Wavelet properties
      2. 2.7.2 Popular wavelet families
    8. 2.8 Summary
  11. 3 Wavelet packet modulator
    1. 3.1 Modulation techniques for wireless communication
      1. 3.1.1 Single-carrier transmission
    2. 3.2 Orthogonal frequency division multiplexing
    3. 3.3 Filter bank multi-carrier methods
      1. 3.3.1 Filtered multi-tone (FMT)
      2. 3.3.2 Cosine modulated multi-tone (CMT)
      3. 3.3.3 OFDM-offset QAM/staggered multi-tone (SMT)
    4. 3.4 Wavelet and wavelet-packet-based multi-carrier modulators
      1. 3.4.1 Wavelet packet modulator (WPM)
      2. 3.4.2 Variants of wavelet packet modulator
      3. 3.4.3 Interpolated tree orthogonal multiplexing (ITOM)
    5. 3.5 Summary
  12. 4 Synchronization issues of wavelet radio
    1. 4.1 Introduction
    2. 4.2 Frequency offset in multi-carrier modulation
      1. 4.2.1 Modelling frequency offset errors
      2. 4.2.2 Frequency offset in OFDM
      3. 4.2.3 Frequency offset in WPM
      4. 4.2.4 Numerical results for frequency offset
    3. 4.3 Phase noise in multi-carrier modulation
      1. 4.3.1 Modelling the phase noise
      2. 4.3.2 Phase noise in OFDM
      3. 4.3.3 Phase noise in WPM
      4. 4.3.4 Numerical results for phase noise
    4. 4.4 Time offset in multi-carrier modulation
      1. 4.4.1 Modelling time offset errors
      2. 4.4.2 Time offset in OFDM
      3. 4.4.3 Time synchronization error in WPM
      4. 4.4.4 Modulation scheme
      5. 4.4.5 Numerical results for time offset
    5. 4.5 Summary
  13. 5 Peak-to-average power ratio
    1. 5.1 Background
    2. 5.2 Introduction
    3. 5.3 PAPR distribution of multi-carrier signal
      1. 5.3.1 OFDM
      2. 5.3.2 WPM
    4. 5.4 PAPR reduction techniques
      1. 5.4.1 Signal-scrambling techniques
      2. 5.4.2 Signal-distortion techniques
      3. 5.4.3 Criteria for selection of PAPR reduction technique
    5. 5.5 Selected mapping with phase modification
      1. 5.5.1 Description of algorithm
      2. 5.5.2 Numerical results
    6. 5.6 Summary
  14. 6 Wavelets for spectrum sensing in cognitive radio applications
    1. 6.1 Background
    2. 6.2 Spectrum sensing in cognitive radio
    3. 6.3 Spectrum sensing methods
      1. 6.3.1 Periodogram
      2. 6.3.2 Correlogram
    4. 6.4 Advantages and disadvantages of conventional spectrum sensing techniques in cognitive radio
      1. 6.4.1 Pilot detection via matched filtering
      2. 6.4.2 Energy detection
      3. 6.4.3 Cyclostationary feature detection
      4. 6.4.4 Multi-taper spectrum estimation (MTSE)
      5. 6.4.5 Filter bank spectrum estimation (FBSE)
    5. 6.5 Advantages of wavelets in spectrum estimation
    6. 6.6 Performance evaluation of spectrum sensing in cognitive radio
      1. 6.6.1 Basic principle of energy detector
      2. 6.6.2 Evaluation of receiver operating characteristic (ROC)
    7. 6.7 Wavelet packet spectrum estimator (WPSE)
      1. 6.7.1 Evaluation of ROC performance of WPSE
    8. 6.8 An efficient model of wavelet-packet based spectrum estimator
      1. 6.8.1 WPSE model
      2. 6.8.2 Study of the detection performance of the developed model
    9. 6.9 Wavelet-packet-based spectrum estimator (WPSE) and compressed sensing
      1. 6.9.1 Introduction to compressed sensing
      2. 6.9.2 Compressed sensing and WPSE
    10. 6.10 Summary
  15. 7 Optimal wavelet design for wireless communications
    1. 7.1 Introduction
    2. 7.2 Criteria for design of wavelets
      1. 7.2.1 Design procedure
      2. 7.2.2 Filter bank implementation of WPM
      3. 7.2.3 Important wavelet properties
      4. 7.2.4 Degrees of freedom to design
    3. 7.3 Example 1 – Maximally frequency selective wavelets
      1. 7.3.1 Formulation of design problem
      2. 7.3.2 Transformation of non-convex problem to linear/convex problem
      3. 7.3.3 Reformulation of optimization problem in the Q(ω) function domain
      4. 7.3.4 Solving the convex optimization problem
      5. 7.3.5 Results and analysis
    4. 7.4 Example 2 – Wavelets with low cross-correlation error
      1. 7.4.1 Time offset errors in WPM
      2. 7.4.2 Formulation of design problem
      3. 7.4.3 Transformation of the mathematical constraints from a non-convex problem to a convex/linear one
      4. 7.4.4 Results and analysis
    5. 7.5 Summary
  16. 8 Conclusion
    1. 8.1 Study of wavelet radio performance under loss of synchronization
    2. 8.2 PAPR performance studies
    3. 8.3 Wavelet-based spectrum sensing for cognitive radio
    4. 8.4 Design of wavelets
    5. 8.5 Future research topics
      1. 8.5.1 Study of WPM performance under loss of synchronization
      2. 8.5.2 PAPR performance studies
      3. 8.5.3 Equalization of channel
      4. 8.5.4 Wavelet packet spectrum estimator (WPSE)
      5. 8.5.5 Design of wavelets
    6. 8.6 Related studies
    7. 8.7 Beyond this book
      1. 8.7.1 Wavelet-based modelling of time-variant wireless channels
      2. 8.7.2 Multiple-access communication
      3. 8.7.3 Wavelet radio for green communication
      4. 8.7.4 Wavelet-based multiple-input–multiple-output communications (MIMO)
    8. 8.8 Concluding remarks
  17. Appendix 1: Semi-definitive programming
  18. Appendix 2: Spectral factorization
  19. Appendix 3: Sum of squares of cross-correlation
  20. Index