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Compressive Sensing for Wireless Communication : Challenges and Opportunities.

By: Sankararajan, Radha.
Material type: materialTypeLabelBookPublisher: Aalborg : River Publishers, 2016Copyright date: �2016Edition: 1st ed.Description: 1 online resource (494 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9788793379862.Subject(s): Wireless communication systemsGenre/Form: Electronic books.Online resources: Click to View
Contents:
Intro -- Front Cover -- Half Title -- RIVER PUBLISHERS SERIES IN COMMUNICATIONS -- Title page - Compressive Sensingf or Wireless Communication: Challenges and Opportunities -- Copyright Page -- Content -- Preface -- Acknowledgement -- List of Figures -- List of Tables -- List of Algorithms -- List of Abbreviations -- Chapter 1 - Introduction -- 1.1 Overview -- 1.2 Motivation -- 1.3 Traditional Sampling -- 1.4 Conventional Data Acquisition System -- 1.4.1 Data Acquisition System -- 1.4.2 Functional Components of DAQ -- 1.4.3 Digital Image Acquisition -- 1.5 Transform Coding -- 1.5.1 Need for Transform Coding -- 1.5.2 Drawbacks of Transform Coding -- 1.6 Compressed Sensing -- 1.6.1 Sparsity and Signal Recovery -- 1.6.2 CS Recovery Algorithms -- 1.6.3 Compressed Sensing for Audio -- 1.6.4 Compressed Sensing for Image -- 1.6.5 Compressed Sensing for Video -- 1.6.6 Compressed Sensing for Computer Vision -- 1.6.7 Compressed Sensing for Cognitive Radio Networks -- 1.6.8 Compressed Sensing for Wireless Networks -- 1.6.9 Compressed Sensing for Wireless Sensor Networks -- 1.7 Book Outline -- References -- Chapter 2 - Compressed Sensing: Sparsity and Signal Recovery -- 2.1 Introduction -- 2.2 Compressed Sensing -- 2.2.1 Compressed Sensing Process -- 2.2.2 What Is the Need for Compressed Sensing? -- 2.2.3 Adaptations of CS Theory -- 2.2.4 Mathematical Background -- 2.2.5 Sparse Filtering and Dynamic Compressed Sensing -- 2.3 Signal Representation -- 2.3.1 Sparsity -- 2.4 Basis Vectors -- 2.4.1 Fourier Transform -- 2.4.2 Discrete Cosine Transform -- 2.4.3 DiscreteWavelet Transform -- 2.4.4 Curvelet Transform -- 2.4.5 Contourlet Transform -- 2.4.6 Surfacelet Transform -- 2.4.7 Karhunen-Lo�eve Theorem -- 2.5 Restricted Isometry Property -- 2.6 Coherence -- 2.7 Stable Recovery -- 2.8 Number of Measurements -- 2.9 Sensing Matrix -- 2.9.1 Null-Space Conditions.
2.9.2 Restricted Isometry Property -- 2.9.3 Gaussian Matrix -- 2.9.4 Toeplitz and Circulant Matrix -- 2.9.5 Binomial Sampling Matrix -- 2.9.6 Structured Random Matrix -- 2.9.7 Kronecker Product Matrix -- 2.9.8 Combination Matrix -- 2.9.9 Hybrid Matrix -- 2.10 Sparse Recovery Algorithms -- 2.10.1 Signal Recovery in Noise -- 2.11 Applications of Compressed Sensing -- 2.12 Summary -- References -- Chapter 3 - Recovery Algorithms -- 3.1 Introduction -- 3.2 Conditions for Perfect Recovery -- 3.2.1 Sensing Matrices -- 3.2.1.1 Null-space conditions -- 3.2.1.2 The restricted isometry property -- 3.2.2 Sensing Matrix Constructions -- 3.3 L1 Minimization -- 3.3.1 L1 Minimization Algorithms -- 3.4 Greedy Algorithms -- 3.4.1 Matching Pursuit (MP) -- 3.4.1.1 Orthogonal matching pursuit (OMP) -- 3.4.1.2 Directional pursuits -- 3.4.1.3 Gradient pursuits -- 3.4.1.4 StOMP -- 3.4.1.5 ROMP -- 3.4.1.6 CoSaMP -- 3.4.1.7 Subspace pursuit (SP) -- 3.5 Iterative Hard Thresholding -- 3.5.1 Empirical Comparisons -- 3.6 FOCUSS -- 3.7 MUSIC -- 3.8 Model-based Algorithms -- 3.8.1 Model-based CoSaMP -- 3.8.2 Model-based IHT -- 3.9 Non-Iterative Algorithms for Image-Processing Applications -- 3.9.1 Advantages of Non-Iterative Algorithms -- 3.9.2 Non-Iterative Procedures for Recovery -- 3.9.2.1 Procedure I -- 3.9.2.2 Procedure II -- 3.9.2.3 Procedure III -- 3.9.3 NITRA -- 3.9.4 R3A -- 3.9.4.1 R3A-based StOMP -- 3.9.5 SPMT -- 3.9.5.1 SPMT for reconstruction of images and videos -- 3.10 Summary -- References -- Chapter 4 - Compressive Sensing for Audio and Speech Signals -- 4.1 Introduction -- 4.1.1 Issues in Applying CS and Sparse Decompositions to Speech and Audio Signals -- 4.2 Multiple Sensors Audio Model -- 4.2.1 Reconstruction of Real, Non-Sparse Audio Signals -- 4.2.2 Detection and Estimation of Truly Sparse Audio Signals.
4.3 Compressive Sensing Framework for Speech Signal Synthesis -- 4.3.1 DFT and LPC Transform Domain -- 4.3.2 Hybrid Dictionary -- 4.3.3 Level of Sparsity -- 4.3.4 Remarks -- 4.4 CS Reconstruction of the Speech and Musical Signals -- 4.4.1 Recovery of Audio Signals with Compressed Sensing -- 4.5 Noise Reduction in Speech and Audio Signals -- 4.5.1 Data Sparsity of Speech Signals -- 4.5.2 Formulation of the Optimization Problem for Speech Noise Reduction -- 4.5.3 Solutions to the Optimization Problem -- 4.6 DCT Compressive Sampling of Frequency-Sparse Audio Signals -- 4.6.1 Performance of Compressive Sensing for Speech Signal with Combined Basis -- 4.7 Single-Channel and Multi-Channel Sinusoidal Audio Coding Using CS -- 4.7.1 Sinusoidal Model -- 4.7.2 Single-Channel Sinusoidal Selection -- 4.7.3 Multi-Channel Sinusoidal Selection -- 4.8 Compressive Sensing for Speech Signal with Orthogonal Symmetric Toeplitz Matrix -- 4.8.1 Orthogonal Symmetric Toeplitz Matrices (OSTM) -- 4.9 Sparse Representations for Speech Recognition -- 4.9.1 An EBW Compressed Sensing Algorithm -- 4.9.2 Line Search A-Functions -- 4.9.3 An Analysis of Sparseness and Regularization in Exemplar-based Methods for Speech Classification -- 4.10 Speaker Identification Using Sparsely Excited Speech Signals and Compressed Sensing -- 4.10.1 Sparsely Excited Speech -- 4.10.2 GMM Speaker Identification -- 4.10.3 Speaker Identification Using CS -- 4.11 Joint Speech-Encoding Technology Based on Compressed Sensing -- 4.11.1 Joint Speech-Encoding Scheme -- 4.11.2 Wavelet Transform -- 4.11.3 PCM -- 4.12 Applications of Compressed Sensing to Speech Coding Based on Sparse Linear Prediction -- 4.12.1 Compressed Sensing Formulation for Speech Coding -- 4.13 Summary -- References -- Chapter 5 - Compressive Sensing for Images -- 5.1 Introduction -- 5.2 Compressive Sensing for Image Fusion.
5.2.1 Multi-Resolution Image Fusion -- 5.2.2 Multi-Focus Image Fusion -- 5.3 Compressive Sensing for Image Compression -- 5.4 Compressive Sensing for Image Denoising -- 5.5 Compressive Sensing Image Reconstruction -- 5.6 Compressive Sensing for Imaging Applications -- 5.6.1 Compressive Magnetic Resonance Imaging -- 5.6.2 Compressive Synthetic Aperture Radar Imaging -- 5.6.3 Compressive Passive MillimeterWave Imaging -- 5.6.4 Compressive Light Transport System -- 5.7 Single-Pixel Camera -- 5.8 Lensless Imaging by Compressive Sensing -- 5.8.1 Lensless Imaging Architecture -- 5.8.1.1 Compressive measurements -- 5.8.1.2 Selection of aperture assembly -- 5.8.2 Prototype for Lensless Imaging -- 5.9 Case Study: Image Transmission in WMSN -- 5.10 Summary -- References -- Chpater 6 - Compressive Sensing for Computer Vision -- 6.1 Introduction -- 6.2 Object Detection Techniques -- 6.2.1 Optical Flow -- 6.2.2 Temporal Difference -- 6.2.3 Background Subtraction -- 6.3 Object-Tracking Techniques -- 6.3.1 Point Tracking -- 6.3.2 Kernel Tracking -- 6.3.3 Silhouette Tracking -- 6.4 Compressive Video Processing -- 6.4.1 CS Based on the DCT Approach -- 6.4.2 CS Based on the DWT Approach -- 6.4.3 CS Based on the Hybrid DWT-DCT Approach -- 6.5 Compressive Sensing for Background Subtraction -- 6.6 Compressive Sensing for Object Detection -- 6.6.1 Sparsity of Background Subtracted Images -- 6.6.2 The Background Constraint -- 6.6.3 Object Detector Based on CS -- 6.6.4 Foreground Reconstruction -- 6.6.5 Adaptation of the Background Constraint -- 6.7 Compressive Sensing for Object Recognition -- 6.8 Compressive Sensing Target Tracking -- 6.8.1 Kalman Filtered Compressive Sensing -- 6.8.2 Joint Compressive Video Coding and Analysis -- 6.8.3 Compressive Sensing for Multi-ViewTracking -- 6.8.4 Compressive Particle Filtering.
6.9 Surveillance Video Processing Using Compressive Sensing -- 6.10 Performance Metrics -- 6.11 Summary -- References -- Chapter 7 - Compressed Sensing for Wireless Networks -- 7.1 Wireless Networks -- 7.1.1 Categories of Wireless Networks -- 7.1.1.1 3G cellular networks -- 7.1.1.2 WiMAX network -- 7.1.1.3 WiFi networks -- 7.1.1.4 Wireless Ad hoc networks -- 7.1.1.5 Wireless sensor networks -- 7.1.2 Advanced Wireless Technologies -- 7.1.2.1 OFDM technology -- 7.1.2.2 Multiple antenna systems -- 7.2 CS-based Wireless Communication -- 7.2.1 Multi-Path Channel Estimation -- 7.2.1.1 Channel model and training-based model -- 7.2.1.2 Compressed channel sensing -- 7.2.2 Random Field Estimation -- 7.2.2.1 Random field model -- 7.2.2.2 Matrix completion algorithm -- 7.2.3 Other Channel Estimation Models -- 7.2.3.1 Blind channel estimation -- 7.2.3.2 Adaptive algorithm -- 7.2.3.3 Group sparsity method -- 7.3 Multiple Access -- 7.3.1 Multiuser Detection -- 7.3.1.1 Comparison between multiuser detection and compressive sensing -- 7.3.1.2 Algorithm for multiuser detection -- 7.3.2 Multiuser Access in Cellular Systems -- 7.3.2.1 Uplink -- 7.3.2.2 Downlink -- 7.4 Summary -- References -- Chapter 8 - Compressive Spectrum Sensing for Cognitive Radio Networks -- 8.1 Introduction -- 8.2 Cognitive Radio and Dynamic Spectrum Access -- 8.2.1 Dynamic Spectrum Access -- 8.2.2 Cognitive Radio -- 8.2.3 Cognitive Radio Architectures -- 8.2.4 Physical Architecture of Cognitive Radio -- 8.3 Spectrum Sensing for Cognitive Radio -- 8.3.1 Spectrum Sensing Techniques -- 8.3.2 Cooperative Spectrum Sensing -- 8.4 Compressed Sensing in Cognitive Radio -- 8.5 Collaborative Compressed Spectrum Sensing -- 8.6 Distributed Compressed Spectrum Sensing -- 8.7 Compressive Sensing for Wideband Cognitive Radios -- 8.8 Research Challenges -- 8.8.1 Sparse Basis Selection.
8.8.2 Adaptive Wideband Sensing.
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Intro -- Front Cover -- Half Title -- RIVER PUBLISHERS SERIES IN COMMUNICATIONS -- Title page - Compressive Sensingf or Wireless Communication: Challenges and Opportunities -- Copyright Page -- Content -- Preface -- Acknowledgement -- List of Figures -- List of Tables -- List of Algorithms -- List of Abbreviations -- Chapter 1 - Introduction -- 1.1 Overview -- 1.2 Motivation -- 1.3 Traditional Sampling -- 1.4 Conventional Data Acquisition System -- 1.4.1 Data Acquisition System -- 1.4.2 Functional Components of DAQ -- 1.4.3 Digital Image Acquisition -- 1.5 Transform Coding -- 1.5.1 Need for Transform Coding -- 1.5.2 Drawbacks of Transform Coding -- 1.6 Compressed Sensing -- 1.6.1 Sparsity and Signal Recovery -- 1.6.2 CS Recovery Algorithms -- 1.6.3 Compressed Sensing for Audio -- 1.6.4 Compressed Sensing for Image -- 1.6.5 Compressed Sensing for Video -- 1.6.6 Compressed Sensing for Computer Vision -- 1.6.7 Compressed Sensing for Cognitive Radio Networks -- 1.6.8 Compressed Sensing for Wireless Networks -- 1.6.9 Compressed Sensing for Wireless Sensor Networks -- 1.7 Book Outline -- References -- Chapter 2 - Compressed Sensing: Sparsity and Signal Recovery -- 2.1 Introduction -- 2.2 Compressed Sensing -- 2.2.1 Compressed Sensing Process -- 2.2.2 What Is the Need for Compressed Sensing? -- 2.2.3 Adaptations of CS Theory -- 2.2.4 Mathematical Background -- 2.2.5 Sparse Filtering and Dynamic Compressed Sensing -- 2.3 Signal Representation -- 2.3.1 Sparsity -- 2.4 Basis Vectors -- 2.4.1 Fourier Transform -- 2.4.2 Discrete Cosine Transform -- 2.4.3 DiscreteWavelet Transform -- 2.4.4 Curvelet Transform -- 2.4.5 Contourlet Transform -- 2.4.6 Surfacelet Transform -- 2.4.7 Karhunen-Lo�eve Theorem -- 2.5 Restricted Isometry Property -- 2.6 Coherence -- 2.7 Stable Recovery -- 2.8 Number of Measurements -- 2.9 Sensing Matrix -- 2.9.1 Null-Space Conditions.

2.9.2 Restricted Isometry Property -- 2.9.3 Gaussian Matrix -- 2.9.4 Toeplitz and Circulant Matrix -- 2.9.5 Binomial Sampling Matrix -- 2.9.6 Structured Random Matrix -- 2.9.7 Kronecker Product Matrix -- 2.9.8 Combination Matrix -- 2.9.9 Hybrid Matrix -- 2.10 Sparse Recovery Algorithms -- 2.10.1 Signal Recovery in Noise -- 2.11 Applications of Compressed Sensing -- 2.12 Summary -- References -- Chapter 3 - Recovery Algorithms -- 3.1 Introduction -- 3.2 Conditions for Perfect Recovery -- 3.2.1 Sensing Matrices -- 3.2.1.1 Null-space conditions -- 3.2.1.2 The restricted isometry property -- 3.2.2 Sensing Matrix Constructions -- 3.3 L1 Minimization -- 3.3.1 L1 Minimization Algorithms -- 3.4 Greedy Algorithms -- 3.4.1 Matching Pursuit (MP) -- 3.4.1.1 Orthogonal matching pursuit (OMP) -- 3.4.1.2 Directional pursuits -- 3.4.1.3 Gradient pursuits -- 3.4.1.4 StOMP -- 3.4.1.5 ROMP -- 3.4.1.6 CoSaMP -- 3.4.1.7 Subspace pursuit (SP) -- 3.5 Iterative Hard Thresholding -- 3.5.1 Empirical Comparisons -- 3.6 FOCUSS -- 3.7 MUSIC -- 3.8 Model-based Algorithms -- 3.8.1 Model-based CoSaMP -- 3.8.2 Model-based IHT -- 3.9 Non-Iterative Algorithms for Image-Processing Applications -- 3.9.1 Advantages of Non-Iterative Algorithms -- 3.9.2 Non-Iterative Procedures for Recovery -- 3.9.2.1 Procedure I -- 3.9.2.2 Procedure II -- 3.9.2.3 Procedure III -- 3.9.3 NITRA -- 3.9.4 R3A -- 3.9.4.1 R3A-based StOMP -- 3.9.5 SPMT -- 3.9.5.1 SPMT for reconstruction of images and videos -- 3.10 Summary -- References -- Chapter 4 - Compressive Sensing for Audio and Speech Signals -- 4.1 Introduction -- 4.1.1 Issues in Applying CS and Sparse Decompositions to Speech and Audio Signals -- 4.2 Multiple Sensors Audio Model -- 4.2.1 Reconstruction of Real, Non-Sparse Audio Signals -- 4.2.2 Detection and Estimation of Truly Sparse Audio Signals.

4.3 Compressive Sensing Framework for Speech Signal Synthesis -- 4.3.1 DFT and LPC Transform Domain -- 4.3.2 Hybrid Dictionary -- 4.3.3 Level of Sparsity -- 4.3.4 Remarks -- 4.4 CS Reconstruction of the Speech and Musical Signals -- 4.4.1 Recovery of Audio Signals with Compressed Sensing -- 4.5 Noise Reduction in Speech and Audio Signals -- 4.5.1 Data Sparsity of Speech Signals -- 4.5.2 Formulation of the Optimization Problem for Speech Noise Reduction -- 4.5.3 Solutions to the Optimization Problem -- 4.6 DCT Compressive Sampling of Frequency-Sparse Audio Signals -- 4.6.1 Performance of Compressive Sensing for Speech Signal with Combined Basis -- 4.7 Single-Channel and Multi-Channel Sinusoidal Audio Coding Using CS -- 4.7.1 Sinusoidal Model -- 4.7.2 Single-Channel Sinusoidal Selection -- 4.7.3 Multi-Channel Sinusoidal Selection -- 4.8 Compressive Sensing for Speech Signal with Orthogonal Symmetric Toeplitz Matrix -- 4.8.1 Orthogonal Symmetric Toeplitz Matrices (OSTM) -- 4.9 Sparse Representations for Speech Recognition -- 4.9.1 An EBW Compressed Sensing Algorithm -- 4.9.2 Line Search A-Functions -- 4.9.3 An Analysis of Sparseness and Regularization in Exemplar-based Methods for Speech Classification -- 4.10 Speaker Identification Using Sparsely Excited Speech Signals and Compressed Sensing -- 4.10.1 Sparsely Excited Speech -- 4.10.2 GMM Speaker Identification -- 4.10.3 Speaker Identification Using CS -- 4.11 Joint Speech-Encoding Technology Based on Compressed Sensing -- 4.11.1 Joint Speech-Encoding Scheme -- 4.11.2 Wavelet Transform -- 4.11.3 PCM -- 4.12 Applications of Compressed Sensing to Speech Coding Based on Sparse Linear Prediction -- 4.12.1 Compressed Sensing Formulation for Speech Coding -- 4.13 Summary -- References -- Chapter 5 - Compressive Sensing for Images -- 5.1 Introduction -- 5.2 Compressive Sensing for Image Fusion.

5.2.1 Multi-Resolution Image Fusion -- 5.2.2 Multi-Focus Image Fusion -- 5.3 Compressive Sensing for Image Compression -- 5.4 Compressive Sensing for Image Denoising -- 5.5 Compressive Sensing Image Reconstruction -- 5.6 Compressive Sensing for Imaging Applications -- 5.6.1 Compressive Magnetic Resonance Imaging -- 5.6.2 Compressive Synthetic Aperture Radar Imaging -- 5.6.3 Compressive Passive MillimeterWave Imaging -- 5.6.4 Compressive Light Transport System -- 5.7 Single-Pixel Camera -- 5.8 Lensless Imaging by Compressive Sensing -- 5.8.1 Lensless Imaging Architecture -- 5.8.1.1 Compressive measurements -- 5.8.1.2 Selection of aperture assembly -- 5.8.2 Prototype for Lensless Imaging -- 5.9 Case Study: Image Transmission in WMSN -- 5.10 Summary -- References -- Chpater 6 - Compressive Sensing for Computer Vision -- 6.1 Introduction -- 6.2 Object Detection Techniques -- 6.2.1 Optical Flow -- 6.2.2 Temporal Difference -- 6.2.3 Background Subtraction -- 6.3 Object-Tracking Techniques -- 6.3.1 Point Tracking -- 6.3.2 Kernel Tracking -- 6.3.3 Silhouette Tracking -- 6.4 Compressive Video Processing -- 6.4.1 CS Based on the DCT Approach -- 6.4.2 CS Based on the DWT Approach -- 6.4.3 CS Based on the Hybrid DWT-DCT Approach -- 6.5 Compressive Sensing for Background Subtraction -- 6.6 Compressive Sensing for Object Detection -- 6.6.1 Sparsity of Background Subtracted Images -- 6.6.2 The Background Constraint -- 6.6.3 Object Detector Based on CS -- 6.6.4 Foreground Reconstruction -- 6.6.5 Adaptation of the Background Constraint -- 6.7 Compressive Sensing for Object Recognition -- 6.8 Compressive Sensing Target Tracking -- 6.8.1 Kalman Filtered Compressive Sensing -- 6.8.2 Joint Compressive Video Coding and Analysis -- 6.8.3 Compressive Sensing for Multi-ViewTracking -- 6.8.4 Compressive Particle Filtering.

6.9 Surveillance Video Processing Using Compressive Sensing -- 6.10 Performance Metrics -- 6.11 Summary -- References -- Chapter 7 - Compressed Sensing for Wireless Networks -- 7.1 Wireless Networks -- 7.1.1 Categories of Wireless Networks -- 7.1.1.1 3G cellular networks -- 7.1.1.2 WiMAX network -- 7.1.1.3 WiFi networks -- 7.1.1.4 Wireless Ad hoc networks -- 7.1.1.5 Wireless sensor networks -- 7.1.2 Advanced Wireless Technologies -- 7.1.2.1 OFDM technology -- 7.1.2.2 Multiple antenna systems -- 7.2 CS-based Wireless Communication -- 7.2.1 Multi-Path Channel Estimation -- 7.2.1.1 Channel model and training-based model -- 7.2.1.2 Compressed channel sensing -- 7.2.2 Random Field Estimation -- 7.2.2.1 Random field model -- 7.2.2.2 Matrix completion algorithm -- 7.2.3 Other Channel Estimation Models -- 7.2.3.1 Blind channel estimation -- 7.2.3.2 Adaptive algorithm -- 7.2.3.3 Group sparsity method -- 7.3 Multiple Access -- 7.3.1 Multiuser Detection -- 7.3.1.1 Comparison between multiuser detection and compressive sensing -- 7.3.1.2 Algorithm for multiuser detection -- 7.3.2 Multiuser Access in Cellular Systems -- 7.3.2.1 Uplink -- 7.3.2.2 Downlink -- 7.4 Summary -- References -- Chapter 8 - Compressive Spectrum Sensing for Cognitive Radio Networks -- 8.1 Introduction -- 8.2 Cognitive Radio and Dynamic Spectrum Access -- 8.2.1 Dynamic Spectrum Access -- 8.2.2 Cognitive Radio -- 8.2.3 Cognitive Radio Architectures -- 8.2.4 Physical Architecture of Cognitive Radio -- 8.3 Spectrum Sensing for Cognitive Radio -- 8.3.1 Spectrum Sensing Techniques -- 8.3.2 Cooperative Spectrum Sensing -- 8.4 Compressed Sensing in Cognitive Radio -- 8.5 Collaborative Compressed Spectrum Sensing -- 8.6 Distributed Compressed Spectrum Sensing -- 8.7 Compressive Sensing for Wideband Cognitive Radios -- 8.8 Research Challenges -- 8.8.1 Sparse Basis Selection.

8.8.2 Adaptive Wideband Sensing.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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