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Compressive Sensing for Wireless Networks

Overview

Compressive sensing is a new signal processing paradigm that aims to encode sparse signals by using far lower sampling rates than those in the traditional Nyquist approach. It helps acquire, store, fuse and process large data sets efficiently and accurately. This method, which links data acquisition, compression, dimensionality reduction and optimization, has attracted significant attention from researchers and engineers in various areas. This comprehensive reference develops a unified view on how to incorporate ...

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Compressive Sensing for Wireless Networks

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Overview

Compressive sensing is a new signal processing paradigm that aims to encode sparse signals by using far lower sampling rates than those in the traditional Nyquist approach. It helps acquire, store, fuse and process large data sets efficiently and accurately. This method, which links data acquisition, compression, dimensionality reduction and optimization, has attracted significant attention from researchers and engineers in various areas. This comprehensive reference develops a unified view on how to incorporate efficiently the idea of compressive sensing over assorted wireless network scenarios, interweaving concepts from signal processing, optimization, information theory, communications and networking to address the issues in question from an engineering perspective. It enables students, researchers and communications engineers to develop a working knowledge of compressive sensing, including background on the basics of compressive sensing theory, an understanding of its benefits and limitations, and the skills needed to take advantage of compressive sensing in wireless networks.

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Product Details

  • ISBN-13: 9781107018839
  • Publisher: Cambridge University Press
  • Publication date: 5/31/2013
  • Pages: 312
  • Product dimensions: 7.10 (w) x 9.70 (h) x 0.80 (d)

Meet the Author

Zhu Han is an Associate Professor in the Electrical and Computer Engineering Department at the University of Houston, Texas. He received an NSF CAREER award in 2010 and the IEEE Fred W. Ellersick Prize in 2011. He co-authored papers that won the best paper award at the IEEE International Conference on Communications 2009, the 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt09), and the IEEE Wireless Communication and Networking Conference, 2012.

Husheng Li is an Assistant Professor in the Electrical and Computer Engineering Department at the University of Tennessee. He received the Best Paper Award of the EURASIP Journal on Wireless Communications and Networking in 2005 (together with his PhD advisor, Professor H. V. Poor), the Best Demo Award of IEEE Globecom in 2010, and the Best Paper Award at IEEE ICC in 2011.

Wotao Yin is an Associate Professor at the Department of Computational and Applied Mathematics at Rice University. He won an NSF CAREER award in 2008 and an Alfred P. Sloan Research Fellowship in 2009.

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Table of Contents

1. Introduction; 2. Overview of wireless networks; Part I. Compressive Sensing Techniques: 3. Compressive sensing framework; 4. Sparse optimization algorithms; Part II. Compressive Sensing-Based Wireless Communication: 5. Analog to digital converter; 6. Channel estimation; 7. Ultra wide band; 8. Positioning; 9. Multiple access; 10. Cognitive radio networks and sensor networks.

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