Compressive Measurement of Spread Spectrum Signals

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Graduate College

Grantor dc:publisher

The University of Arizona.

Year dc:date.issued

2015

Author dc:creator

• Liu, Feng

Advisor dc:contributor.advisor

• Bilgin, Ali

Committee members dc:contributor.committeemember

• Bilgin, Ali
• Marcellin, Michael W.
• Vasic, Bane

• Decoding
• Detection
• Spread Spectrum
• Electrical & Computer Engineering
• Compressive Sensing

Statement dc: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.

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10150/347310

OAI identifier oai:identifier

oai:repository.arizona.edu:10150/347310