Persistent Link:
http://hdl.handle.net/10150/347310
Title:
Compressive Measurement of Spread Spectrum Signals
Author:
Liu, Feng
Issue Date:
2015
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Spread Spectrum (SS) techniques are methods used in communication systems where the spectra of the signal is spread over a much wider bandwidth. The large bandwidth of the resulting signals make SS signals difficult to intercept using conventional methods based on Nyquist sampling. Recently, a novel concept called compressive sensing has emerged. Compressive sensing theory suggests that a signal can be reconstructed from much fewer measurements than suggested by the Shannon Nyquist theorem, provided that the signal can be sparsely represented in a dictionary. In this work, motivated by this concept, we study compressive approaches to detect and decode SS signals. We propose compressive detection and decoding systems based both on random measurements (which have been the main focus of the CS literature) as well as designed measurement kernels that exploit prior knowledge of the SS signal. Compressive sensing methods for both Frequency-Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS) systems are proposed.
Type:
text; Electronic Dissertation
Keywords:
Decoding; Detection; Spread Spectrum; Electrical & Computer Engineering; Compressive Sensing
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical & Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Bilgin, Ali

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleCompressive Measurement of Spread Spectrum Signalsen_US
dc.creatorLiu, Fengen_US
dc.contributor.authorLiu, Fengen_US
dc.date.issued2015-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractSpread Spectrum (SS) techniques are methods used in communication systems where the spectra of the signal is spread over a much wider bandwidth. The large bandwidth of the resulting signals make SS signals difficult to intercept using conventional methods based on Nyquist sampling. Recently, a novel concept called compressive sensing has emerged. Compressive sensing theory suggests that a signal can be reconstructed from much fewer measurements than suggested by the Shannon Nyquist theorem, provided that the signal can be sparsely represented in a dictionary. In this work, motivated by this concept, we study compressive approaches to detect and decode SS signals. We propose compressive detection and decoding systems based both on random measurements (which have been the main focus of the CS literature) as well as designed measurement kernels that exploit prior knowledge of the SS signal. Compressive sensing methods for both Frequency-Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS) systems are proposed.en_US
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectDecodingen_US
dc.subjectDetectionen_US
dc.subjectSpread Spectrumen_US
dc.subjectElectrical & Computer Engineeringen_US
dc.subjectCompressive Sensingen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical & Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorBilgin, Alien_US
dc.contributor.committeememberBilgin, Alien_US
dc.contributor.committeememberMarcellin, Michael W.en_US
dc.contributor.committeememberVasic, Baneen_US
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