Analog-to-information conversion via random demodulation

Sami Kirolos, Jason Laska, Michael Wakin, Marco Duarte, Dror Baron, Tamer Ragheb, Yehia Massoud, Richard Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

365 Scopus citations

Abstract

Many problems in radar and communication signal processing involve radio frequency (RF) signals of very high bandwidth. This presents a serious challenge to systems that might attempt to use a high-rate analog-to-digital converter (ADC) to sample these signals, as prescribed by the Shannon/Nyquist sampling theorem. In these situations, however, the information level of the signal is often far lower than the actual bandwidth, which prompts the question of whether more efficient schemes can be developed for measuring such signals. In this paper we propose a system that uses modulation, filtering, and sampling to produce a low-rate set of digital measurements. Our "analog-to-information converter" (AIC) is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal having a sparse representation in some dictionary can be recovered from a small number of linear projections of that signal. We generalize the CS theory to continuous-time sparse signals, explain our proposed AIC system in the CS context, and discuss practical issues regarding implementation. © 2006 IEEE.
Original languageEnglish (US)
Title of host publication2006 IEEE Dallas/CAS Workshop onDesign, Applications, Integration and Software, DCAS-06
Pages71-74
Number of pages4
DOIs
StatePublished - Dec 1 2006
Externally publishedYes

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