Compressive Sensing for Wireless Communication

Compressive Sensing for Wireless Communication

Compressed Sensing (CS) is a promising method that recovers the sparse and compressible signals from severely under-sampled measurements. CS can be applied to wireless communication to enhance its capabilities. As this technology is proliferating, it is possible to explore its need and benefits for...

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Detalles Bibliográficos
Autor principal: Sankararajan, Radha (-)
Otros Autores: Rajendran, Hemalatha, Sukumaran, Aasha Nandhini
Formato: Libro electrónico
Idioma:Inglés
Publicado: Aalborg : River Publishers 2016.
Colección:EBSCO Academic eBook Collection Complete.
River Publishers Series in Communications.
Acceso en línea:Conectar con la versión electrónica
Ver en Universidad de Navarra:https://innopac.unav.es/record=b47413128*spi
Tabla de Contenidos:
  • 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-Loève 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.

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