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OverviewMany natural signals possess only a few degrees of freedom. For instance, the occupied radio spectrum may be intermittently concentrated to only a few frequency bands of the system bandwidth. This special structural feature - signal sparsity - is conducive in designing efficient signal processing techniques for wireless networks. In particular, the signal sparsity can be leveraged by the recently emerged joint sampling and compression paradigm, compressed sensing (CS). This monograph reviews several recent CS advancements in wireless networks with an aim to improve the quality of signal reconstruction or detection while reducing the use of energy, radio, and computation resources. The monograph covers a diversity of compressive data reconstruction, gathering, and detection frameworks in cellular, cognitive, and wireless sensor networking systems. The monograph first gives an overview of the principles of CS for the readers unfamiliar with the topic. For the researchers knowledgeable in CS, the monograph provides in-depth reviews of several interesting CS advancements in designing tailored CS reconstruction techniques for wireless applications. The monograph can serve as a basis for the researchers intended to start working in the field, and altogether, lays a foundation for further research in the covered areas. Full Product DetailsAuthor: Markus Leinonen , Marian Codreanu , Georgios B. GiannakisPublisher: now publishers Inc Imprint: now publishers Inc Weight: 0.435kg ISBN: 9781680836462ISBN 10: 1680836463 Pages: 310 Publication Date: 29 November 2019 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contents1. Introduction 2. Fundamentals of Compressed Sensing I. Advanced Signal Reconstruction from Compressive Measurements 3. Online Adaptive Estimation of Sparse Signals: Where RLS Meets the l1-Norm 4. Sparsity-Cognizant Total Least-Squares for Perturbed Compressive Sampling II. Compressive Data Gathering in Wireless Sensor Networks 5. Compressed Acquisition of Correlated Streaming Sensor Data 6. Distributed Source Coding via Quantized Compressed Sensing 7. Rate-Distortion Performance of Lossy Compressed Sensing III. Sparsity-Enabled Cognitive and Cellular Communicationse 8. Channel Gain Cartography for Cognitive Radios Leveraging Low Rank and Sparsity 9. Exploiting Sparse User Activity in Multiuser Detection 10. Summary Acknowledgements ReferencesReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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