Compressive Sensing for Wireless Communication: Challenges and Opportunities

by Radha Sankararajan, Hemalatha Rajendran, Aasha Nandhini Sukumaran

Overview

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.

Product Details

ISBN-13:	9781000791228
Publisher:	River Publishers
Publication date:	09/01/2022
Sold by:	Barnes & Noble
Format:	eBook
Pages:	494
File size:	11 MB
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About the Author

Radha Sankararajan, Hemalatha Rajendran, Aasha Nandhini Sukumaran

Table of Contents

Preface, Acknowledgement, List of Figures, List of Tables, List of Algorithms, List of Abbreviations, 1. Introduction, 2. Compressed Sensing: Sparsity and Signal Recovery, 3. Recovery Algorithms, 4. Compressive Sensing for Audio and Speech Signals, 5. Compressive Sensing for Images, 6. Compressive Sensing for Computer Vision, 7. Compressed Sensing for Wireless Networks, 8. Compressive Spectrum Sensing for Cognitive Radio Networks, 9. Compressive Sensing forWireless Sensor Networks, 10. Efficient Sampling and Reconstruction Strategy for CS in WMSNS, 11. Real-World Applications of Compressive Sensing, Index, About the Authors