Compressive Sensing for Wireless Communication.

Compressive Sensing for Wireless Communication.

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Bibliographic Details
Author / Creator:Sankararajan, Radha.
Imprint:Aalborg : River Publishers, 2016.
Description:1 online resource (494 pages)
Language:English
Series:River Publishers Series in Communications
River Publishers series in communications.
Subject:
Wireless communication systems.
Antennas (Electronics)
Microwaves.
Software radio.
Antennas (Electronics)
Microwaves.
Software radio.
Wireless communication systems.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/13540527
Hidden Bibliographic Details
Other authors / contributors:Rajendran, Hemalatha.
Sukumaran, Aasha Nandhini.
ISBN:9788793379862
8793379862
9781003337652
1003337651
9781000794366
1000794369
100079122X
9781000791228
9788793379855
8793379854
Notes:Radha Sankararajan, Hemalatha Rajendran, Aasha Nandhini Sukumaran
Print version record.
Summary: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 emerging applicationsCompressive Sensing for Wireless Communication provides: A clear insight into the basics of compressed sensing A thorough exploration of applying CS to audio, image and computer vision Different dimensions of applying CS in Cognitive radio networks CS in wireless sensor network for spatial compression and projection Real world problems/projects that can be implemented and tested Efficient methods to sample and reconstruct the images in resource constrained WMSN environmentThis book provides the details of CS and its associated applications in a thorough manner. It lays a direction for students and new engineers and prepares them for developing new tasks within the field of CS. It is an indispensable companion for practicing engineers who wish to learn about the emerging areas of interest.
Other form:Print version: Sankararajan, Radha. Compressive Sensing for Wireless Communication: Challenges and Opportunities. Aalborg : River Publishers, ©2016 9788793379855
Standard no.:10.1201/9781003337652
Table of 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-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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