Compressive Sensing for Wireless Networks

This comprehensive reference delivers the understanding and skills needed to take advantage of compressive sensing in wireless networks.

Zhu Han (Author), Husheng Li (Author), Wotao Yin (Author)

9781107018839, Cambridge University Press

Hardback, published 6 June 2013

304 pages, 100 b/w illus. 7 tables
25.3 x 18 x 2 cm, 0.79 kg

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.

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.

Subject Areas: Signal processing [UYS], WAP [wireless technology TJKW], Communications engineering / telecommunications [TJK], Electronics & communications engineering [TJ], Electrical engineering [THR]